Wire hunting method and device

Abstract

The embodiment of the invention provides a wire hunting method and device. The method comprises the following steps: respectively determining first current data and second current data of a first sampling point and a second sampling point on a wire to be confirmed at a target time point; according to the first current data and the second current data, time domain similarity features of the first current data and the second current data are determined; and according to the time domain similarity characteristics of the first current data and the second current data, determining that the to-be-confirmed leads between the first sampling point and the second sampling point are the same lead. According to the method, under the condition that power supply is not interrupted, the wire connection condition is accurately recognized, and meanwhile negative effects on an original circuit are avoided.

Description

Technical Field

[0001] The present application relates to the technical field of live wire tracing, and in particular to a wire tracing method and device. Background Art

[0002] In critical facilities such as servers, communication equipment, and important medical equipment, the stability and reliability of power supply are extremely important because these devices need to operate 24 hours a day. Any unplanned power outage or line error may have serious consequences. Therefore, it is crucial to ensure that the line is connected correctly during inspection and maintenance, and disconnecting the line by mistake will bring high risks.

[0003] Currently, the line-finding equipment on the market mainly uses two methods to identify wires: power failure detection and applying additional electrical signals. Power failure detection cuts off the power supply and uses a test signal or voltage detection tool to check the wire connection to avoid interfering with normal operation. Applying an electrical signal injects a specific frequency signal into the circuit and uses a receiver to track the signal to determine the wire position and connection status based on the signal strength and path. These two methods can help technicians accurately identify and locate wires in complex lines, ensuring efficient inspection and maintenance.

[0004] However, since power failure or application of an electrical signal may affect the use of the device, the existing line-finding method has the problem of poor use effect when used. Summary of the invention

[0005] The embodiments of the present application provide a method and device for finding a conductor, so as to avoid affecting the use of equipment, thereby improving the use effect of finding the conductor.

[0006] In a first aspect, an embodiment of the present application provides a method for finding a conductor, comprising:

[0007] Respectively determine first current data and second current data of a first sampling point and a second sampling point on the wire to be confirmed at a target time point;

[0008] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data;

[0009] According to the time domain similarity characteristics of the first current data and the second current data, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

[0010] In a possible implementation manner, when the time domain similarity feature includes a correlation coefficient characterizing the correlation degree between the first current data and the second current data,

[0011] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0012] Determine first current average data and second current average data, wherein the first current average data is obtained by weighted averaging the first current data at each target time point, and the second current average data is obtained by weighted averaging the second current data at each target time point;

[0013] Obtaining first difference data according to the first current average data and the first current data;

[0014] Obtain second difference data according to the second current average data and the second current data;

[0015] A correlation coefficient representing the degree of correlation between the first current data and the second current data is determined according to the first difference data and the second difference data at each target time point.

[0016] In a possible implementation manner, a correlation coefficient characterizing the correlation degree between the first current data and the second current data satisfies:

[0017]

[0018] The value range of i is 1 to n, n is the number of target time points, S i ′ Characterized as the i-th first current data, Characterized as the first current average data, s i ′ Characterized as the i-th second current data, Characterized as the second current average data.

[0019] In a possible implementation manner, when the time domain similarity feature includes a mean square error representing the first current data and the second current data,

[0020] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0021] determining difference data according to the first current data and the second current data;

[0022] The difference data at each target time point is weighted and averaged to obtain a mean square error representing the first current data and the second current data.

[0023] In a possible implementation manner, when the time domain similarity feature includes a frequency spectrum correlation coefficient characterizing the first current data and the second current data,

[0024] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0025] Determine first transformation data and second transformation data, the first transformation data is obtained by performing discrete Fourier transformation processing on the first current data, and the second transformation data is obtained by performing discrete Fourier transformation processing on the second current data;

[0026] A frequency spectrum correlation coefficient representing the first current data and the second current data is obtained according to the first transformed data and the first average transformed data, and the second transformed data and the second average transformed data.

[0027] In a possible implementation manner, the frequency spectrum correlation coefficient characterizing the first current data and the second current data satisfies:

[0028]

[0029] in, is the first transformation data, is the first mean transformed data, is the second transformation data, is the second average transformed data.

[0030] In a possible implementation manner, the first current data and the second current data are normalized current data.

[0031] In a possible implementation, determining that the to-be-confirmed wire between the first sampling point and the second sampling point is the same wire according to the time domain similarity characteristics of the first current data and the second current data includes:

[0032] Determine the weight of the time domain similarity feature;

[0033] Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and a weight of the time domain similarity feature;

[0034] According to the similarity score, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

[0035] In a possible implementation manner, when the time domain similarity feature includes a correlation coefficient representing the correlation degree between the first current data and the second current data, a mean square error representing the first current data and the second current data, and a frequency spectrum correlation coefficient representing the first current data and the second current data,

[0036] Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and the weight of the time domain similarity feature includes:

[0037] A first score is obtained according to the correlation coefficient and a first weight in the weights;

[0038] A second score is obtained according to the correlation coefficient and a second weight in the weight;

[0039] A third score is obtained according to the correlation coefficient and the third weight in the weight;

[0040] The first score, the second score, and the third score are summed to obtain a similarity score between the first current data and the second current data.

[0041] In a second aspect, an embodiment of the present application provides a wire finding device, the device comprising:

[0042] A current data determination module, used to respectively determine first current data and second current data of a first sampling point and a second sampling point on the wire to be confirmed at a target time point;

[0043] An analysis module, configured to determine a time domain similarity feature of the first current data and the second current data based on the first current data and the second current data;

[0044] The determination module is used to determine that the wire to be confirmed between the first sampling point and the second sampling point is the same wire according to the time domain similarity characteristics of the first current data and the second current data.

[0045] In a third aspect, an embodiment of the present application provides a line-finding device, including: a memory, a processor;

[0046] Memory stores computer-executable instructions;

[0047] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0048] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0049] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0050] The wire finding method and device provided in the embodiments of the present application respectively determine the first current data and the second current data of the first sampling point and the second sampling point on the to-be-confirmed wire in use at the target time point, and determine that the to-be-confirmed wire between the first sampling point and the second sampling point is the same wire based on the time domain similarity characteristics between the first current data and the second current data, so as to achieve the effect of accurately identifying the wire connection status without interrupting the power supply of the device, while avoiding negative impact on the original circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] Figure 1 A schematic diagram of a scenario of a wire finding method provided in the present application;

[0053] Figure 1a A schematic diagram of a connection structure between a TMR sensor probe and a wire provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the flow of the wire finding method provided by the present application;

[0055] Figure 3 A schematic diagram of the structure of a wire-finding device for a conductor provided in the present application;

[0056] Figure 4 A schematic diagram of the structure of a wire-finding device for a conductor provided in this application.

[0057] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0058] 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 the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0059] First, the terms involved in this application are explained:

[0060] Discrete Fourier Transform (DFT) is a mathematical tool used to convert a finite sequence of time domain signals into a frequency domain representation. That is, DFT converts a set of values ​​at discrete time points (usually signal samples obtained from actual measurements or digital sampling) into the same number of discrete frequency components. In this way, the distribution of signals at different frequencies can be analyzed, which is crucial for filtering, spectrum analysis, data compression, and many other applications.

[0061] Most of the wire-finding devices currently on the market mainly use power-off detection or adding additional electrical signals to the circuit to identify wires. However, these methods have the problems of needing to interrupt the power supply and possibly causing electromagnetic interference, which cannot meet the needs of equipment that does not allow power outages, and also bring safety hazards. Due to the lack of an effective method and tool for interference-free wire-finding for live wires, accidents in which important equipment loses power due to mistaken disconnection of lines during equipment maintenance frequently occur. Therefore, researching and developing a method that can accurately identify live wires without interfering with normal operations is crucial to preventing safety accidents caused by mistaken disconnection of lines and ensuring stable operation of important equipment during maintenance.

[0062] The wire finding method provided in the embodiment of the present application is a method for finding a wire. Since the cross-sections of the same unbranched wire at different positions have the same current magnitude at the same time, the current is detected at two sampling points of the suspected same wire. If the currents detected at the two sampling points at multiple times are equal, it can be determined that the two sampling points belong to the same wire, thereby achieving the effect of accurately identifying the connection status of the wire without interrupting the power supply of the device, while avoiding negative impact on the original circuit.

[0063] Figure 1 A schematic diagram of a scenario of a wire finding method provided in this application, such as Figure 1 As shown, the scene includes a device for a wire-finding method applied to a wire and a live wire, wherein the device for a wire-finding method applied to a wire includes a sensor probe and a detection device, wherein there are two sensor probes, which are respectively installed at the two ends of the live wire, each sensor probe is connected to a detection device, and the two detection devices can transmit the data completed by the detection to the processor device for analysis and processing by wire or wireless means, so as to automatically determine whether the two sampling points are the same wire according to the results of the analysis and processing, and display the results accordingly.

[0064] Among them, the sensor probe can be a TMR sensor probe, which can refer to a design based on the Tunnel Magnetoresistance (TMR) effect, which can detect magnetic field changes with high sensitivity. The TMR sensor probe can be composed of two layers of magnetic material separated by a very thin insulating layer. When the external magnetic field changes, the spin state of the electrons passing through the insulating layer changes, resulting in a significant change in the resistance value. Due to its high sensitivity, high resolution and low power consumption, the TMR sensor probe is widely used in magnetic field sensing, position detection, angle measurement, biomedical engineering and other fields, providing reliable technical support for precise measurement.

[0065] In some embodiments, the TMR sensor probe is fixed to the wire sampling point using a non-contact method, wherein the non-contact fixing method includes but is not limited to magnetic adsorption, elastic clamping or bundling and other non-invasive installation methods.

[0066] for example, Figure 1a A schematic diagram of the connection structure between the TMR sensor probe and the wire provided in the embodiment of the present application, as shown in FIG. Figure 1a As shown, the connection structure includes a live wire 110, a strapping tape 120 made of a flexible magnetic field shielding material, a TMR sensor 130, a strapping tape fixing clip 140, a probe body 150, and a connecting line 160 between the probe and the detection device. When in use, the TMR sensor 130 can be extended and retracted in the cavity. When there is no external pressure, the TMR sensor 130 extends outward for a section. When there is an external object such as a wire squeezing, the TMR sensor 130 shrinks into the cavity. This design can ensure that after the wires are tied and fixed, the end face of the TMR sensor 130 is in close contact with the outer skin of the live wire 110, thereby ensuring the accuracy of the detection. One end of the strapping tape 120 made of a flexible magnetic field shielding material is fixedly connected to the probe body 150, and the other end can be freely loosened. When bundling, the live wire 110 is encircled therein, and the free end passes through the strapping tape fixing clip 140. After tightening, the fixing clip can clamp the free end of the strapping tape 120 to prevent it from loosening, and the bundling is completed. When the measurement is finished, the fixing clamp 140 is manually loosened to loosen the free end of the binding belt 120, and the encircled live wire 110 can be loosened at the same time. The binding belt 120 is made of a flexible magnetic field shielding material, which can shield the interference of the environmental magnetic field and can bundle live wires 110 of different thicknesses, and can also be bundled in a narrow space.

[0067] Therefore, the TMR sensor probe provided in the embodiment of the present application can achieve non-invasive fixation, can make the TMR sensor in close contact with the outer sheath of the wire, can be used in places with narrow space, and can be used for bundling and fixing wires of various wire diameters.

[0068] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0069] Figure 2 A schematic diagram of the flow of the wire finding method provided by the present application, such as Figure 2 As shown, the method includes:

[0070] S201 , respectively determining first current data and second current data of a first sampling point and a second sampling point on a to-be-confirmed wire at a target time point.

[0071] The wire to be confirmed may refer to a specific wire that needs to be identified and located in a complex circuit system to ensure that the wire line is not accidentally disconnected or incorrectly connected during inspection and maintenance. In an embodiment of the present application, the wire to be confirmed may refer to a wire between a first sampling point and a second sampling point.

[0072] The sampling point may refer to a point selected by the user in a complex circuit system. By setting a sensor at the point, the current data flowing through the point can be obtained.

[0073] The current data may refer to the current intensity value collected and recorded at the sampling point. The target time point may refer to the time when the current intensity value is collected and recorded at the sampling point. The target time point may be composed of multiple fixed or non-fixed time points. For example, the target time point may be multiple time points sampled at a fixed time (10 milliseconds).

[0074] In the embodiment of the present application, since there are multiple target time points, there are also multiple first current data and second current data obtained. Therefore, when recording data, the data can be recorded as (T, S), that is, the sampling time T and the sampling result S. The sampling time is the target time point of this sampling detection, which can be accurate to milliseconds or microseconds according to the actual situation, and the sampling result is the detection result of the target time point. Therefore, it is conducive to the calculation of similarity. It should be noted that for alternating current with a higher detection frequency, the detection time interval should be as short as possible. This parameter depends on the response speed of the sensor and the frequency of AD conversion.

[0075] Among them, for detection in different situations (such as detection of direct current, low-frequency alternating current or high-frequency alternating current), detection equipment with different performances can be selected for processing. For example, when used to detect direct current and low-frequency alternating current, the performance of TMR sensors and AD conversion devices can be selected for processing.

[0076] In an embodiment of the present application, after the operator issues a command to start detection, the detection equipment used for detection executes an automatic sampling program, and the devices located at the first sampling point and the second sampling point start detection at the same time, and perform the next sampling at a fixed interval, sampling n times. The data collected from the first sampling point are (T1, S1), (T2, S2), (T3, S3)...(Tn-1, Sn-1), (Tn, Sn), and the data collected from the second sampling point are (t1, s1), (t2, s2), (t3, s3)...(tn-1, sn-1), (tn, sn).

[0077] S202: Determine time-domain similarity features of the first current data and the second current data according to the first current data and the second current data.

[0078] The time domain similarity feature may refer to a characteristic or index used to measure the similarity between two signals in the time domain (i.e., the domain represented by the original signal). The similarity may be evaluated by comparing the waveform shape, amplitude change, or other characteristics of the two signals in the same time period.

[0079] In an embodiment of the present application, the time domain similarity feature may include: a correlation coefficient characterizing the correlation degree between the first current data and the second current data, a mean square error characterizing the first current data and the second current data, and a spectral correlation coefficient characterizing the first current data and the second current data.

[0080] In the embodiment of the present application, when the time domain similarity feature includes a correlation coefficient characterizing the correlation degree between the first current data and the second current data,

[0081] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0082] Determine first current average data and second current average data, wherein the first current average data is obtained by weighted averaging the first current data at each target time point, and the second current average data is obtained by weighted averaging the second current data at each target time point;

[0083] Obtaining first difference data according to the first current average data and the first current data;

[0084] Obtain second difference data according to the second current average data and the second current data;

[0085] A correlation coefficient representing the degree of correlation between the first current data and the second current data is determined according to the first difference data and the second difference data at each target time point.

[0086] The correlation coefficient characterizing the correlation between the first current data and the second current data can be used as a statistical indicator to measure the strength of the linear relationship between the first current data and the second current data, and the value is between -1 and 1. In the embodiment of the present application, the correlation coefficient characterizing the correlation between the first current data and the second current data satisfies:

[0087]

[0088] The value range of i is 1 to n, n is the number of target time points, S i ′ Characterized as the i-th first current data, Characterized as the first current average data, s i ′ Characterized as the i-th second current data, Characterized as the second current average data.

[0089] In the embodiment of the present application, the first current data and the second current data are current data after normalization processing.

[0090] That is, the first current average data is obtained by weighted averaging the first current data after normalization, and the first difference data at each target time point is obtained by subtracting the first current average data from the first current data after normalization; the second current average data is obtained by weighted averaging the second current data after normalization, and the second difference data at each target time point is obtained by subtracting the second current average data from the second current data after normalization.

[0091] In the embodiment of the present application, when the time domain similarity feature includes a mean square error representing the first current data and the second current data,

[0092] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0093] determining difference data according to the first current data and the second current data;

[0094] The difference data at each target time point is weighted and averaged to obtain a mean square error representing the first current data and the second current data.

[0095] Among them, the mean square error (MSE) is a statistical indicator used to measure the degree of difference between two sets of data.

[0096] Among them, the mean square error MSE satisfies:

[0097]

[0098] Among them, S i ′ and i ′ They may be normalized first current data and second current data, respectively.

[0099] In the embodiment of the present application, when the time domain similarity feature includes a frequency spectrum correlation coefficient characterizing the first current data and the second current data,

[0100] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0101] Determine first transformation data and second transformation data, the first transformation data is obtained by performing discrete Fourier transformation processing on the first current data, and the second transformation data is obtained by performing discrete Fourier transformation processing on the second current data;

[0102] A frequency spectrum correlation coefficient representing the first current data and the second current data is obtained according to the first transformed data and the first average transformed data, and the second transformed data and the second average transformed data.

[0103] Among them, the spectrum correlation coefficient can be a statistical indicator to measure the similarity between two sets of data in the frequency domain.

[0104] In the embodiment of the present application, the first current data and the second current data are first subjected to discrete Fourier transform to obtain first transformed data in the form of spectrum representation. and the second transformed data Then, the correlation coefficient of the two spectrum vectors is calculated in the frequency domain, that is, the following formula is used to obtain the spectrum correlation coefficient representing the first current data and the second current data. The formula satisfies:

[0105]

[0106] in, is the first transformation data, is the first mean transformed data, is the second transformation data, is the second average transformed data.

[0107] The first current data and the second current data subjected to discrete Fourier transform are the first current data and the second current data after normalization processing.

[0108] In the embodiment of the present application, the process after normalizing the first current data and the second current data can be: 1. First, the current data of the first sampling point and the second sampling point are aligned in the time domain to ensure that the sampling times of the data corresponding to the 1st to nth sampling points of the two sampling points are equal. For example, check T1=t1, T2=t2, T3=t3, ..., and remove the unequal data.

[0109] 2. Normalize the current data after time domain alignment. In order to retain the relative amplitude relationship between the two sets of data and the distribution characteristics of the data, the embodiment of the present application can adopt the minimum and maximum normalization (Min-Max Scaling) method to perform overall normalization processing on the data (S1, S2, S3, ..., Sn-1, Sn) and (s1, s2, s3, ..., sn-1, sn). If the data of the two sampling points are n, then among the 2n data, the maximum value is maxS and the minimum value is minS, then these 2n data are sequentially substituted into the formula S'=(S-minS) / (maxS-minS) to obtain the normalized data (S'1, S'2, S'3...S'n-1, S'n) and (s'1, s'2, s'3...s'n-1, s'n). These data range between 0 and 1 to facilitate subsequent calculations.

[0110] S203: Determine, based on time-domain similarity characteristics of the first current data and the second current data, that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

[0111] When the time-domain similarity feature meets the preset similarity requirement, that is, if the time-domain similarity feature indicates that the first current data and the second current data are highly similar, it can be considered that the wire between the two sampling points is continuous and belongs to the same wire.

[0112] When the time domain similarity feature does not meet the preset similarity requirement, that is, if the similarity feature shows that the two sets of current data are very different, it may mean that there is a disconnection, damage or other problems, causing them to not belong to the same conductor.

[0113] In the embodiment of the present application, determining that the wire to be confirmed between the first sampling point and the second sampling point is the same wire according to the time domain similarity characteristics of the first current data and the second current data includes:

[0114] Determine the weight of the time domain similarity feature;

[0115] Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and a weight of the time domain similarity feature;

[0116] According to the similarity score, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

[0117] There are multiple coefficients in the time domain similarity feature, and the similarity score between the first current data and the second current data can be obtained according to the weight of each coefficient.

[0118] When the similarity score meets the preset similarity requirement, that is, is greater than the similarity score threshold, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire. When the similarity score does not meet the preset similarity requirement, that is, is less than or equal to the similarity score threshold, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is not the same wire.

[0119] In the embodiment of the present application, when the time domain similarity feature includes a correlation coefficient representing the correlation degree between the first current data and the second current data, a mean square error representing the first current data and the second current data, and a spectral correlation coefficient representing the first current data and the second current data,

[0120] Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and the weight of the time domain similarity feature includes:

[0121] A first score is obtained according to the correlation coefficient and a first weight in the weights;

[0122] A second score is obtained according to the correlation coefficient and a second weight in the weight;

[0123] A third score is obtained according to the correlation coefficient and the third weight in the weight;

[0124] The first score, the second score, and the third score are summed to obtain a similarity score between the first current data and the second current data.

[0125] Among them, since the three similarity features of the correlation coefficient characterizing the correlation degree between the first current data and the second current data, the mean square error characterizing the first current data and the second current data, and the spectral correlation coefficient characterizing the first current data and the second current data have different meanings, they need to be transformed and weighted averaged to obtain a comprehensive similarity evaluation value S that more accurately characterizes the similarity score.

[0126] Among them, for the correlation coefficient r, its value is between -1 and 1, where 1 indicates a complete positive correlation, -1 indicates a complete negative correlation, and 0 indicates no linear relationship. If the two sampling points are on the same wire, the two sets of data must be positively correlated, and the calculated value must be close to 1, so r can be transformed:

[0127]

[0128] Thus, the transformed value r can be obtained ′ , for r ′ Assign weight ω r .

[0129] For the mean square error MSE, since its calculated value has no fixed range, the smaller the better, so the MSE is transformed:

[0130]

[0131] Thus, the transformed value MSE of the mean square error MSE is obtained ′ , for MSE ′ Assign weight ω MSE .

[0132] For the spectral correlation coefficient R, since its value is between -1 and 1, where 1 indicates a complete positive correlation, -1 indicates a complete negative correlation, and 0 indicates no linear relationship, if the two sampling points are on the same wire, the two sets of data must be positively correlated, and the calculated value must be close to 1, so R is transformed:

[0133]

[0134] Thus, the transformed value R can be obtained ′ , for R ′ Assign weight ω R .

[0135] In summary, the comprehensive similarity S can be calculated, where the comprehensive similarity S satisfies:

[0136] S=ω r × ′ +ω MSE ×MSE ′ +ω R ×R ′ ;

[0137] Among them, the weight value must satisfy ω r +ω MSE +ω R =1, in the embodiment of the present application, the value of each weight value can be determined according to the actual usage, for example, ω r =0.5,ω MSE =0.3,ω R =0.2. Thus, the range of the S value is between 0 and 1. The closer it is to 1, the higher the similarity between the two sets of data, and the greater the probability that the two sampling points are the same wire. The closer it is to 0, the lower the similarity between the two sets of data, and the greater the probability that the two sampling points are different wires.

[0138] After obtaining the comprehensive similarity S, the comprehensive similarity S can be compared with the similarity score threshold. When S is not lower than the similarity score threshold, it means that the two sampling points belong to the same conductor. When S is lower than the similarity score threshold, it means that the two sampling points do not belong to the same conductor.

[0139] The similarity score threshold can be set as needed, for example, the similarity score threshold can be 0.9. In some embodiments, the similarity score threshold can also be an empirical value set based on experience, and the similarity score threshold is obtained based on practical experiments.

[0140] After determining whether the wire to be confirmed between the first sampling point and the second sampling point is the same wire, the determination result can be clearly given on the device screen or by lighting an indicator light, thereby facilitating reading by the operator.

[0141] In some embodiments, to improve the reliability of the results, steps S201 to S203 may be repeated multiple times during the test process, and an average value of the multiple results may be taken as the final result.

[0142] In some embodiments, steps S201 to S203 can all be completed by automatically running the program. After manually bundling and fixing the device at the sampling point, the operating device can start the program to automatically complete the detection.

[0143] The wire finding method provided in the embodiment of the present application pre-connects and synchronizes the equipment, synchronously collects data within a period of time, performs time domain alignment and normalization on the data, calculates similarity features and comprehensive similarity, and finally determines whether two sampling points are the same wire by comparing with a threshold. Therefore, only one staff member is required to operate, which does not affect the operation of the line, and is easy to operate in actual application. It can be applied to wire finding and identification of direct current, low-frequency alternating current, and important equipment and key circuits that are not allowed to be powered off.

[0144] Figure 3 A schematic diagram of the structure of the wire-finding device provided in this application, such as Figure 3 As shown, the wire finding device 30 provided in this embodiment includes:

[0145] A current data determination module 301 is used to respectively determine first current data and second current data of a first sampling point and a second sampling point on a to-be-confirmed wire at a target time point;

[0146] An analysis module 302 is used to determine a time domain similarity feature of the first current data and the second current data according to the first current data and the second current data;

[0147] The determination module 303 is used to determine that the wire to be confirmed between the first sampling point and the second sampling point is the same wire according to the time domain similarity characteristics of the first current data and the second current data.

[0148] When the time-domain similarity feature includes a correlation coefficient characterizing the correlation degree between the first current data and the second current data, in a possible implementation manner, the analysis module 302 may also be specifically used for:

[0149] Determining time domain similarity features of the first current data and the second current data according to the first current data and the second current data includes:

[0150] Determine first current average data and second current average data, wherein the first current average data is obtained by weighted averaging the first current data at each target time point, and the second current average data is obtained by weighted averaging the second current data at each target time point;

[0151] Obtaining first difference data according to the first current average data and the first current data;

[0152] Obtain second difference data according to the second current average data and the second current data;

[0153] A correlation coefficient representing the degree of correlation between the first current data and the second current data is determined according to the first difference data and the second difference data at each target time point.

[0154] In a possible implementation, the analysis module 302 may also be specifically used for:

[0155] The correlation coefficient representing the correlation degree between the first current data and the second current data satisfies:

[0156]

[0157] The value range of i is 1 to n, n is the number of target time points, S i ′ Characterized as the i-th first current data, Characterized as the first current average data, s i ′ Characterized as the i-th second current data, Characterized as the second current average data.

[0158] When the time-domain similarity feature includes a mean square error characterizing the first current data and the second current data, in a possible implementation manner, the analysis module 302 may also be specifically used to:

[0159] determining difference data according to the first current data and the second current data;

[0160] The difference data at each target time point is weighted and averaged to obtain a mean square error representing the first current data and the second current data.

[0161] When the time-domain similarity feature includes a frequency spectrum correlation coefficient characterizing the first current data and the second current data, in a possible implementation manner, the analysis module 302 may also be specifically configured to:

[0162] Determine first transformation data and second transformation data, the first transformation data is obtained by performing discrete Fourier transformation processing on the first current data, and the second transformation data is obtained by performing discrete Fourier transformation processing on the second current data;

[0163] A frequency spectrum correlation coefficient representing the first current data and the second current data is obtained according to the first transformed data and the first average transformed data, and the second transformed data and the second average transformed data.

[0164] In a possible implementation manner, the analysis module 302 may also be specifically configured to: characterize a frequency spectrum correlation coefficient of the first current data and the second current data that satisfies:

[0165]

[0166] in, is the first transformation data, is the first mean transformed data, is the second transformation data, is the second average transformed data.

[0167] In a possible implementation manner, the analysis module 302 may also be specifically configured to: the first current data and the second current data are normalized current data.

[0168] In a possible implementation, the determination module 303 may also be specifically configured to:

[0169] Determine the weight of the time domain similarity feature;

[0170] Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and a weight of the time domain similarity feature;

[0171] According to the similarity score, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

[0172] When the time domain similarity feature includes a correlation coefficient representing the correlation degree between the first current data and the second current data, a mean square error representing the first current data and the second current data, and a frequency spectrum correlation coefficient representing the first current data and the second current data,

[0173] In a possible implementation, the determination module 303 may also be specifically configured to:

[0174] A first score is obtained according to the correlation coefficient and a first weight in the weights;

[0175] A second score is obtained according to the correlation coefficient and a second weight in the weight;

[0176] A third score is obtained according to the correlation coefficient and the third weight in the weight;

[0177] The first score, the second score, and the third score are summed to obtain a similarity score between the first current data and the second current data.

[0178] The wire-finding device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be described in detail here.

[0179] Figure 4 This is a schematic diagram of the structure of the line-finding device provided in this application. Figure 4 As shown, the line-finding device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 also includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0180] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above method.

[0181] The specific implementation process of the processor 401 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0182] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0183] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0184] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0185] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0186] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0187] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0189] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0192] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0193] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0194] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for finding a conductor, characterized in that: include: Respectively determine first current data and second current data of a first sampling point and a second sampling point on the wire to be confirmed at a target time point; Determining time-domain similarity features of the first current data and the second current data according to the first current data and the second current data; According to the time domain similarity characteristics of the first current data and the second current data, it is determined that the to-be-confirmed wire between the first sampling point and the second sampling point is the same wire.

2. The method according to claim 1, characterized in that When the time domain similarity feature includes a correlation coefficient characterizing the correlation between the first current data and the second current data, The determining, based on the first current data and the second current data, time domain similarity features of the first current data and the second current data includes: Determine first current average data and second current average data, wherein the first current average data is obtained by weighted averaging the first current data at each target time point, and the second current average data is obtained by weighted averaging the second current data at each target time point; obtaining first difference data according to the first current average data and the first current data; obtaining second difference data according to the second current average data and the second current data; A correlation coefficient representing a correlation degree between the first current data and the second current data is determined according to the first difference data and the second difference data at each of the target time points.

3. The method according to claim 2, characterized in that The correlation coefficient characterizing the correlation degree between the first current data and the second current data satisfies: The value range of i is 1 to n, n is the number of target time points, S i ′ Characterized as the i-th first current data, Characterized as the first current average data, s i ′ Characterized as the i-th second current data, Characterized as the second current average data.

4. The method according to claim 1, characterized in that: When the time domain similarity feature includes a mean square error characterizing the first current data and the second current data, The determining, based on the first current data and the second current data, time domain similarity features of the first current data and the second current data includes: determining difference data according to the first current data and the second current data; A weighted average process is performed on the difference data at each of the target time points to obtain a mean square error representing the first current data and the second current data.

5. The method according to claim 1, characterized in that When the time domain similarity feature includes a frequency spectrum correlation coefficient characterizing the first current data and the second current data, The determining, based on the first current data and the second current data, time domain similarity features of the first current data and the second current data includes: Determine first transformed data and second transformed data, wherein the first transformed data is obtained by performing discrete Fourier transform processing on the first current data, and the second transformed data is obtained by performing discrete Fourier transform processing on the second current data; A frequency spectrum correlation coefficient characterizing the first current data and the second current data is obtained according to the first transformed data and the first average transformed data, and the second transformed data and the second average transformed data.

6. The method according to claim 5, characterized in that The frequency spectrum correlation coefficient characterizing the first current data and the second current data satisfies: in, is the first transformation data, is the first average transformed data, is the second transformation data, is the second average transformed data.

7. The method according to any one of claims 1 to 6, characterized in that The first current data and the second current data are normalized current data.

8. The method according to claim 1, characterized in that The determining, based on the time domain similarity characteristics of the first current data and the second current data, that the to-be-confirmed wire between the first sampling point and the second sampling point is the same wire includes: Determining the weight of the time domain similarity feature; Determining a similarity score between the first current data and the second current data according to the time domain similarity feature and a weight of the time domain similarity feature; According to the similarity score, it is determined that the wire to be confirmed between the first sampling point and the second sampling point is the same wire.

9. The method according to claim 8, characterized in that When the time domain similarity feature includes a correlation coefficient representing the correlation between the first current data and the second current data, a mean square error representing the first current data and the second current data, and a frequency spectrum correlation coefficient representing the first current data and the second current data, The determining, according to the time domain similarity feature and the weight of the time domain similarity feature, a similarity score between the first current data and the second current data includes: Obtaining a first score according to the correlation coefficient and a first weight among the weights; Obtaining a second score according to the correlation coefficient and a second weight among the weights; Obtaining a third score according to the correlation coefficient and a third weight among the weights; The first score, the second score, and the third score are summed to obtain a similarity score between the first current data and the second current data.

10. A conductor finding device, characterized in that: The device comprises: A current data determination module, used to respectively determine first current data and second current data of a first sampling point and a second sampling point on the wire to be confirmed at a target time point; An analysis module, configured to determine a time domain similarity feature of the first current data and the second current data according to the first current data and the second current data; The determination module is used to determine that the wire to be confirmed between the first sampling point and the second sampling point is the same wire according to the time domain similarity characteristics of the first current data and the second current data.

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