Household variable relationship identification method and system based on characteristic signal waveform

Abstract

The application discloses a kind of based on feature signal waveform's household variable relationship identification method and system, this identification method is after gathering initial data sequence containing feature current signal, by constructing sliding window on initial data sequence, slide every time after window, feature code determination is carried out based on signal waveform in window, if multiple feature codes are obtained continuously and the feature signal constructed is same with the feature current signal sent, it can be determined that the user end of sending signal belongs to the identification terminal of receiving signal, i.e. Compared with the feature code determination based on the single data feature of current signal amplitude in the prior art, the feature code determination based on the signal waveform in the window fully utilizes the waveform data of the feature current signal, greatly improves the data quantity, is beneficial to accurately analyzing the waveform characteristics of the feature current signal, and thus greatly improves the accuracy of the household variable relationship identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household and variable relationship identification, in particular, to a household and variable relationship identification method and system based on feature signal waveform, an electronic device and a computer readable storage medium. BACKGROUND

[0002] Traditional household and variable relationship identification methods include instantaneous power failure method and power consumption data statistical method. However, these two methods have some inherent defects. The instantaneous power failure method requires high quality of the electric meter and has a great impact on power supply quality, which is easy to damage user equipment and cannot be used frequently. The power consumption data statistical method needs to collect a large amount of power consumption data under various environments in the early stage, establish a user identification model, record user power consumption characteristics, and realize household and variable relationship identification by applying different identification models. The advantage of this method is that it does not need additional identification devices and will not interfere with normal power consumption of users. However, the identification rate cannot reach 100%, and the identification accuracy is related to the data collection amount and model matching algorithm, which cannot achieve real-time accurate identification. In order to overcome the shortcomings of traditional household and variable relationship identification methods, household and variable relationship identification methods based on feature current detection have become a research hotspot.

[0003] At present, the household and variable relationship identification method based on feature current detection utilizes the characteristic that low-frequency current characteristic signals are only transmitted in the same line to the transformer direction. The feature current sending circuit is integrated in the carrier module or the end perception unit of the user electric meter. The electric meter sends 833.3Hz low-frequency characteristic current on the power line through load modulation, and loads information through data coding. The identification terminal receives the characteristic current through the current transformer and demodulates the information. The household and variable relationship is determined by comparing with the feature binary information, and the phase is determined by comparing the amplitudes of three-phase current signals. The current characteristic diagram of the PWM (Pluse Width Modulation, pulse width modulation) signal is shown in FIG. 1. The sampling frequency is 833.3Hz, and the duty cycle can be accurately controlled according to needs. For example, the duty cycle is set to 1:3, that is, 30 cycles for one bit, a total of 0.6 seconds. The high level represents signal 1 and the low level represents signal 0. A group of PWM signals can be represented by binary as 1010 1010 1110 1001, and represented by hexadecimal as 0xAAE9. However, in the actual environment, the 833.3hz frequency signal will be disturbed by other devices or loads. The current characteristic diagram of the PWM signal is shown in FIG. 2, which causes the problem of poor identification accuracy when only comparing the feature current intensity (i.e. current signal amplitude). Figure 1 Figure 2 SUMMARY

[0004] ​​The application provides a household variable relationship identification method and system based on a feature signal waveform, an electronic device and a computer readable storage medium, to solve the technical problem of poor recognition accuracy of the existing household variable relationship identification method based on feature current detection.

[0005] According to one aspect of the application, a household variable relationship identification method based on a feature signal waveform is provided, including the following contents:

[0006] An initial data sequence containing a feature current signal is collected;

[0007] A sliding window is constructed, and the length of the sliding window is not less than the periodic variation duration of the feature current signal;

[0008] The sliding window is slid in the initial data sequence, and the sliding step is the periodic variation duration of the feature current signal each time. After each sliding, the signal waveform in the sliding window is determined for a feature code. If multiple feature codes are obtained continuously and the constructed feature signal is the same as the feature current signal, the household variable relationship is obtained.

[0009] Further, the process of determining the signal waveform in the sliding window for a feature code after each sliding specifically includes the following contents:

[0010] The sampling point data sequence of each cycle in the sliding window is subtracted by a power frequency data sequence in the initial data sequence to obtain a feature data sequence of each cycle.

[0011] The fluctuation number and fluctuation value of each feature data sequence are calculated, and it is determined whether each feature data sequence is a feature signal waveform.

[0012] The number of feature data sequences in the sliding window that are determined to be feature signal waveforms is counted. If the number exceeds a first preset threshold, the feature code of the waveform signal in the sliding window is 1, otherwise, it is 0.

[0013] Further, the process of calculating the fluctuation number and fluctuation value of each feature data sequence and determining whether each feature data sequence is a feature signal waveform specifically includes the following contents:

[0014] Each feature data sequence is sampled to obtain a feature sampling sequence.

[0015] The data of the next sampling point in the feature sampling sequence is subtracted by the data of the previous sampling point, wherein the data of the first sampling point is subtracted by the data of the last sampling point in the previous feature sampling sequence to obtain a feature fluctuation sequence.

[0016] counting the number and value of the fluctuations in the characteristic fluctuation sequence, wherein, if the directions of the continuous multiple characteristic points in the characteristic fluctuation sequence are consistent, the multiple characteristic points are marked as a fluctuation, and the value of the fluctuation is the sum of the values of the multiple characteristic points;

[0017] comparing each fluctuation value with a second preset threshold value, if the fluctuation value is greater than the second preset threshold value, marking the fluctuation as an effective fluctuation, counting the number of effective fluctuations in the characteristic fluctuation sequence, if the number of effective fluctuations is greater than a third preset threshold value, determining that the characteristic fluctuation sequence is a characteristic signal waveform.

[0018] Further, the process of determining the characteristic code of the signal waveform in the sliding window after each sliding further includes the following contents:

[0019] recording the maximum fluctuation value in the characteristic fluctuation sequence, and calculating the mean value of the maximum fluctuation values corresponding to the multiple characteristic fluctuation sequences in the sliding window to obtain the amplitude of the characteristic current signal in the sliding window.

[0020] Further, if the characteristic fluctuation sequence is determined to be a non-characteristic signal waveform, the characteristic fluctuation sequence is sequentially sorted from large to small, if the first 1 / 4 bits are less than a fourth preset threshold value, the current characteristic fluctuation sequence is updated to a power frequency data sequence without characteristic current signal.

[0021] Further, if four characteristic codes are all 1 when the sliding window is continuously sliding, the sampling point data sequence of any one cycle in the four sliding windows is updated to a power frequency data sequence without characteristic current signal.

[0022] Further, after obtaining the initial data sequence, the following contents are further included:

[0023] performing linear interpolation on each cycle in the initial data sequence to obtain an interpolation sequence of each cycle, wherein the number of interpolations between adjacent two data points is the same, finding the first zero-crossing data point from the interpolation sequence of each cycle, and equally spacing the data points based on the position of the first zero-crossing data point and the number of interpolations between adjacent two data points to obtain a preprocessed data sequence of each cycle.

[0024] In addition, the present application also provides a household variable relationship identification system based on a characteristic signal waveform, comprising:

[0025] a data acquisition module for acquiring an initial data sequence containing a characteristic current signal;

[0026] a window construction module for constructing a sliding window, the length of the sliding window is not less than the periodic change duration of the characteristic current signal;

[0027] The intelligent recognition module is used to slide a sliding window in the initial data sequence. The sliding step size is the duration of the periodic change of the characteristic current signal. After each slide, the signal waveform in the sliding window is judged by the feature code. If multiple feature codes are obtained in succession and the constructed feature signal is the same as the characteristic current signal, the household-transformation relationship is obtained.

[0028] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0029] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for identifying household change relationships based on characteristic signal waveforms, wherein the computer program executes the steps of the method described above when running on a computer.

[0030] The present invention has the following effects:

[0031] The present invention provides a method for identifying the relationship between a user and a transformer based on characteristic signal waveforms. After acquiring an initial data sequence containing characteristic current signals, a sliding window is constructed and slides across the initial data sequence. After each slide, feature codes are determined based on the signal waveform within the window. If multiple feature codes are obtained consecutively and the constructed feature signal is identical to the transmitted characteristic current signal, it can be determined that the user terminal transmitting the signal belongs to the identification terminal receiving the signal, thus identifying the user-transformer relationship. The present invention's feature code determination based on the signal waveform within the window, compared to existing methods that rely solely on the amplitude (i.e., signal strength) of the current signal, fully utilizes the waveform data of the characteristic current signal, significantly increasing the data volume and facilitating accurate analysis of the waveform characteristics of the characteristic current signal, thereby greatly improving the accuracy of user-transformer relationship identification.

[0032] In addition, the household-transformation relationship identification system based on characteristic signal waveforms of the present invention also has the above-mentioned advantages.

[0033] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0035] Figure 1 This is a schematic diagram of the current characteristics of the PWM signal sent from the user side.

[0036] Figure 2 This is a schematic diagram of the PWM signal current characteristics obtained by the receiving side based on 6.4kHz sampling in a real environment.

[0037] Figure 3 This is a flowchart illustrating a preferred embodiment of the household-transformation relationship identification method based on characteristic signal waveforms according to the present invention.

[0038] Figure 4 yes Figure 3 A schematic diagram of the sub-process of step S3.

[0039] Figure 5 yes Figure 4 A schematic diagram of the sub-process of step S32.

[0040] Figure 6 yes Figure 4 A schematic diagram of another sub-process of step S32.

[0041] Figure 7 This is a schematic diagram of the module structure of a household change relationship identification system based on feature signal waveforms, according to another embodiment of the present invention. Detailed Implementation

[0042] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered below.

[0043] Understandable, such as Figure 3 As shown, a preferred embodiment of the present invention provides a method for identifying household-transformation relationships based on characteristic signal waveforms, including the following:

[0044] Step S1: Acquire an initial data sequence containing the characteristic current signal;

[0045] Step S2: Construct a sliding window, the length of which is not less than the duration of the periodic change of the characteristic current signal;

[0046] Step S3: Use a sliding window to slide in the initial data sequence. The sliding step size is the duration of the periodic change of the characteristic current signal. After each slide, the signal waveform in the sliding window is judged by the feature code. If multiple feature codes are obtained in succession and the constructed feature signal is the same as the characteristic current signal, then the household-transformation relationship is obtained.

[0047] It is understood that the user-transformer relationship identification method based on characteristic signal waveforms in this embodiment, after acquiring an initial data sequence containing characteristic current signals, constructs a sliding window that slides across the initial data sequence. After each slide, feature codes are determined based on the signal waveform within the window. If multiple feature codes are obtained consecutively and the constructed feature signal is the same as the transmitted characteristic current signal, it can be determined that the user terminal transmitting the signal belongs to the identification terminal receiving the signal, i.e., the user-transformer relationship is identified. This invention, based on the signal waveform within the window for feature code determination, compared to existing methods based on the single data feature of current signal amplitude (i.e., signal strength), fully utilizes the waveform data of the characteristic current signal, greatly increasing the data volume and facilitating accurate analysis of the waveform characteristics of the characteristic current signal, thereby significantly improving the accuracy of user-transformer relationship identification.

[0048] It is understood that the PWM signal (i.e., characteristic current signal) used in this invention has a sampling frequency of 833.3Hz, a duty cycle of 1:3, and one bit has 30 cycles, or 0.6 seconds. A high level represents signal 1 and a low level represents signal 0. Therefore, a set of PWM signals can be represented in binary as 10101010 1110 1001, and in hexadecimal as 0xAAE9. Of course, in other embodiments of this invention, the duty cycle can be set to 1:4, 1:5, or other values. This invention only uses a duty cycle of 1:3 as an example and does not impose specific limitations. In step S1, under actual conditions, the identification terminal collects the current data sequence containing the characteristic current signal sent by the user terminal, i.e., the initial data sequence, for example... Figure 2 As shown, the initial data sequence was obtained by sampling at a sampling frequency of 6.4kHz. It can be seen that under the interference of sampling frequency and actual noise signal, the current characteristic waveform exhibits abnormal changes. At this time, the accuracy of identifying the relationship between households and transformers by relying solely on the peak value of the current signal is poor.

[0049] It is understood that in step S2, since the transmitted PWM signal exhibits periodic changes, the length of the sliding window must be no less than the duration of the periodic changes of the characteristic current signal, that is, the length of the sliding window is required to be no less than 30 power frequency waveforms, or no less than 0.6s, so as to facilitate the identification of the household-transformer relationship based on the complete change law of the PWM signal, which is beneficial to improving the identification accuracy.

[0050] It is understood that in step S3, the constructed sliding window slides through the initial data sequence, with each slide step set to 30 cycles, or 0.6 seconds. Then, after each slide, the signal waveform within the window is judged. If it is determined to be a characteristic signal waveform, the characteristic code within the sliding window is 1; if it is determined to be a non-characteristic signal waveform, the characteristic code within the sliding window is 0. By continuously sliding and obtaining characteristic codes within 16 windows, if the constructed characteristic signal is 1010 1010 1110 1001 or 0xAAE9, it indicates that the identification terminal has detected a characteristic current signal, meaning that the meter belongs to the identification terminal, i.e., the household-transformer relationship is identified.

[0051] Among them, such as Figure 4 As shown, the process of determining the feature code of the signal waveform within the sliding window after each slide specifically includes the following:

[0052] Step S31: Obtain the power frequency data sequence without characteristic current signal in the initial data sequence, and subtract the power frequency data sequence from the sampling point data sequence of each cycle in the sliding window to obtain the characteristic data sequence of each cycle;

[0053] Step S32: Calculate the number of fluctuations and fluctuation value of each feature data sequence, and determine whether each feature data sequence is a feature signal waveform;

[0054] Step S33: Count the number of feature data sequences that are determined to be feature signal waveforms within the sliding window. If the number exceeds the first preset threshold, the feature code of the waveform signal within the sliding window is 1; otherwise, it is 0.

[0055] Specifically, in step S31, the power frequency data sequence when there is no characteristic current signal in the initial data sequence is obtained, wherein the power frequency data sequence is a one-cycle data sequence, i.e. Figure 2 The frequency s is then calculated. Next, the data sequence of each sampling point within the sliding window is subtracted from the data sequence of frequency s to obtain the characteristic data sequence of each frequency, which can be expressed as: ΔS = S index -S0, ΔS represents the characteristic data sequence of each cycle, S index S0 represents the data sequence of sampling points for each cycle, and S0 represents the power frequency data sequence without characteristic current signals, i.e., the data sequence of cycle s. It can be understood that identification based on characteristic data sequences can eliminate interference from noise signals, thus improving identification accuracy.

[0056] Then, in step S32, the number of fluctuations and the fluctuation value in each feature data sequence ΔS are calculated, and a comprehensive judgment is made based on the number of fluctuations and the fluctuation value to determine whether each feature data sequence is a feature signal waveform. Optionally, as follows... Figure 5As shown, the process of calculating the number of fluctuations and fluctuation values ​​of each feature data sequence, and determining whether each feature data sequence is a feature signal waveform, specifically includes the following:

[0057] Step S321: Sample each feature data sequence to obtain a feature sampling sequence;

[0058] Step S322: Subtract the previous sampling point data from the next sampling point data in the feature sampling sequence, wherein the first sampling point data corresponds to the subtraction of the last sampling point data in the previous feature sampling sequence, to obtain the feature fluctuation sequence;

[0059] Step S323: Calculate the number of fluctuations and the fluctuation value in the characteristic fluctuation sequence. If multiple consecutive feature points in the characteristic fluctuation sequence have the same direction, it is marked as a fluctuation, and the fluctuation value is the sum of the values ​​of the multiple feature points.

[0060] Step S324: Compare each fluctuation value with the second preset threshold. If the fluctuation value is greater than the second preset threshold, mark the fluctuation as a valid fluctuation. Count the number of valid fluctuations in the feature fluctuation sequence. If the number of valid fluctuations is greater than the third preset threshold, determine the feature fluctuation sequence as a feature signal waveform.

[0061] Specifically, each feature data sequence ΔS is sampled at a high frequency, for example, the sampling frequency is set to 6.4 kHz, to obtain the feature sampling sequence, which can be represented as: {x1, x2, x3, ..., x 128 Then, subtract the previous sampling point from the next sampling point in the feature sampling sequence, where the first sampling point corresponds to the subtraction of the last sampling point in the previous feature sampling sequence, to obtain the feature fluctuation sequence, which can be represented as {Δx1, Δx2, Δx3, ..., Δx}. 128}, where Δx1=x1-x0, Δx2=x2-x1, …, Δx 128 =x 128 -x 127 Where x0 is the last sampling point data of the previous cycle. 128前 Of course, the high-frequency sampling frequency can also be set to 12.8kHz, 25.6kHz, etc. This invention uses a 6.4kHz sampling frequency as an example for illustration, and no specific limitation is made here.

[0062] Then, the characteristic fluctuation sequence {Δx1, Δx2, Δx3, ..., Δx} is calculated. 128 The number and value of fluctuations in}, when multiple consecutive Δx iIf the directions are consistent, meaning all are positive or all are negative, it indicates the existence of a peak or trough, thus constituting a fluctuation. The fluctuation value (i.e., the characteristic peak value) is ε. j =∑Δx i The number of fluctuations and the corresponding fluctuation values ​​for each characteristic fluctuation sequence are counted.

[0063] Next, each fluctuation value is compared with 0.2A. If the fluctuation value is greater than 0.2A, the fluctuation is marked as a valid fluctuation, and the number of valid fluctuations is counted. If the number of valid fluctuations is not less than 16, the characteristic fluctuation sequence is determined to be a characteristic signal waveform, that is, the corresponding characteristic data sequence is a characteristic signal waveform. In this invention, the current amplitude of the transmitted PWM signal is 0.4A, therefore the second preset threshold is set to 0.2A. Of course, in other embodiments of this invention, it can also be set to 0.3A, 0.35A, etc., and can be set according to needs. Furthermore, the number of valid fluctuations must be not less than 16 because there are 16 peak values ​​in the transmitted PWM signal.

[0064] It is understood that in step S33, the number of feature data sequences that are determined to be feature signal waveforms within the sliding window is counted. If the number is greater than the first preset threshold, where the specific value of the first preset threshold can be 25, 26, 27, 28, etc., and can be set according to the accuracy requirements, then the feature code of the waveform signal within the sliding window is set to 1; otherwise, it is set to 0.

[0065] Optionally, such as Figure 6 As shown, the process of determining the feature code of the signal waveform within the sliding window after each slide also includes the following:

[0066] Step S325: Record the maximum fluctuation value in the characteristic fluctuation sequence, and calculate the average value based on the maximum fluctuation values ​​corresponding to multiple characteristic fluctuation sequences within the sliding window to obtain the amplitude of the characteristic current signal within the sliding window.

[0067] It can be understood that each window contains data of at least 30 cycles, and each cycle corresponds to a characteristic fluctuation sequence. By recording the maximum fluctuation value in each characteristic fluctuation sequence and calculating the average value based on the maximum fluctuation values ​​corresponding to at least 30 cycles, the amplitude of the characteristic current signal within the sliding window, i.e., the signal strength, can be obtained.

[0068] Optionally, in step S324, if the characteristic fluctuation sequence is determined to be a non-characteristic signal waveform, the characteristic fluctuation sequence is sorted from largest to smallest. If the value of the first 1 / 4 quantile is less than the fourth preset threshold, for example, 0.1A, the current characteristic fluctuation sequence is updated to a power frequency data sequence without characteristic current signal, i.e., updated to cycle s, and then step S3 is repeated. It can be understood that since the characteristic current signal has periodic changes, and the ΔS obtained by subtracting two non-characteristic signal waveforms should not have large current fluctuations, the above method is adopted to update the cycle s in order to improve the fault tolerance.

[0069] Optionally, in step S3, if four consecutive feature codes are all 1 when the sliding window slides, and the transmitted PWM signal is 1010 1010 1110 1001, with a maximum of three consecutive feature codes being 1, this means that the selection of cycle s is incorrect. That is, the cycle data sequence containing the feature current signal is taken as cycle s, while the cycle data in the sliding window is actually a power frequency data sequence without the feature current signal. In this case, the sampling point data sequence of any one cycle in the four sliding windows is updated to a power frequency data sequence without the feature current signal. Preferably, the first cycle or the last cycle is taken as cycle s, thereby avoiding the situation of incorrect selection of cycle s and further improving the recognition accuracy.

[0070] Optionally, in step S1, after obtaining the initial data sequence, the following content is also included:

[0071] Linear interpolation is performed on each cycle of the initial data sequence to obtain the interpolation sequence of each cycle, wherein the number of interpolations between any two adjacent data points is the same. The data point that first crosses zero is found from the interpolation sequence of each cycle, and points are selected at equal intervals based on the position of the data point that first crosses zero and the number of interpolations between any two adjacent data points to obtain the preprocessed data sequence of each cycle.

[0072] It is understandable that, since the data acquisition is discrete sampling, meaning the sampled data points are discrete sampling points, when a load is present, sending a characteristic current signal will cause inconsistencies in the initial phase angles of the zero-crossing current sampling points. The actual waveform is composed of multiple discrete sampling points connected together, but in reality, the first point position of the zero-crossing point of each cycle sampling point is different. This results in a certain deviation in the waveform obtained by subtracting the non-characteristic signal sequence (i.e., the data sequence of cycle s) from the characteristic signal sequence (i.e., the sampling point sequence containing the characteristic signal waveform within the sliding window), leading to problems in updating cycle s. Therefore, this invention, after obtaining the initial data sequence, performs linear interpolation on each cycle in the initial data sequence to obtain the interpolation sequence for each cycle, where the number of interpolations between adjacent data points is the same. For example, the cycle data sequence of the zero-crossing points in the initial data sequence is represented as: S1 = {x0, x1, x2, x3, ..., x...} 128}, where x0 is a point that does not cross zero, i.e., x0 is less than 0 and x1 is greater than 0. Then, linear interpolation is performed on sequence S1, inserting m values ​​between two adjacent data points to obtain the interpolation sequence S2 = {x0, x1, ..., x2} for each cycle. 01 ,x 02 ,…,x 0m ,x1,x 11 ,x 12 ,…,x 1m ,x2,x3,….,x 128 Then, find the data point x that first crosses zero in the interpolation sequence S2 for each cycle. 0i Then based on x 0i The position and number of interpolations m can construct the preprocessed data sequence S3 = {x} for each cycle. 0i x 1i ,x 2i ,x 3i ,….,x 127i The initial data sequence is preprocessed using this method, and the household-transformation relationship is then identified based on the preprocessed initial data sequence. The value of 'i' may differ for each cycle. Therefore, by performing the above preprocessing on the initial data sequence, this invention can eliminate the error caused by waveform subtraction due to sampling time deviations in different cycles, further improving the identification accuracy.

[0073] In addition, such as Figure 7 As shown, another embodiment of the present invention also provides a household-transformer relationship identification system based on characteristic signal waveforms, preferably employing the household-transformer relationship identification method described above, including:

[0074] The data acquisition module is used to acquire an initial data sequence containing characteristic current signals;

[0075] The window construction module is used to construct a sliding window, the length of which is not less than the duration of the periodic change of the characteristic current signal;

[0076] The intelligent recognition module is used to slide a sliding window in the initial data sequence. The sliding step size is the duration of the periodic change of the characteristic current signal. After each slide, the signal waveform in the sliding window is judged by the feature code. If multiple feature codes are obtained in succession and the constructed feature signal is the same as the characteristic current signal, the household-transformation relationship is obtained.

[0077] It is understood that the user-transformer relationship identification system based on characteristic signal waveforms in this embodiment, after acquiring an initial data sequence containing characteristic current signals, constructs a sliding window that slides across the initial data sequence. After each slide, feature code determination is performed based on the signal waveform within the window. If multiple feature codes are obtained consecutively and the constructed feature signal is the same as the transmitted characteristic current signal, it can be determined that the user terminal transmitting the signal belongs to the identification terminal receiving the signal, i.e., the user-transformer relationship is identified. This invention, based on the signal waveform within the window for feature code determination, compared to existing feature code determination based on the single data feature of current signal amplitude (i.e., signal strength), fully utilizes the waveform data of the characteristic current signal, greatly increasing the data volume and facilitating accurate analysis of the waveform characteristics of the characteristic current signal, thereby significantly improving the accuracy of user-transformer relationship identification.

[0078] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0079] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for identifying household change relationships based on characteristic signal waveforms, wherein the computer program executes the steps of the method described above when running on a computer.

[0080] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for machine execution, and includes digital or analog communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0087] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for identifying household-transformer relationships based on characteristic signal waveforms, characterized in that, Includes the following: Acquire an initial data sequence containing characteristic current signals; Construct a sliding window, the length of which is not less than the duration of the periodic change of the characteristic current signal; A sliding window is used to slide in the initial data sequence. The sliding step size is the duration of the periodic change of the characteristic current signal. After each slide, the signal waveform in the sliding window is judged by feature code. If multiple feature codes are obtained in succession and the constructed feature signal is the same as the characteristic current signal, the household-transformation relationship is obtained. The process of determining the feature code of the signal waveform within the sliding window after each slide specifically includes the following: Obtain the power frequency data sequence without characteristic current signal in the initial data sequence, and subtract the power frequency data sequence from the sampling point data sequence of each cycle in the sliding window to obtain the characteristic data sequence of each cycle; Calculate the number of fluctuations and fluctuation values ​​for each characteristic data sequence, and determine whether each characteristic data sequence is a characteristic signal waveform; The number of feature data sequences that are identified as feature signal waveforms within the sliding window is counted. If the number exceeds the first preset threshold, the feature code of the waveform signal within the sliding window is 1; otherwise, it is 0. The process of calculating the number of fluctuations and fluctuation values ​​of each feature data sequence, and determining whether each feature data sequence is a feature signal waveform, specifically includes the following: Each feature data sequence is sampled to obtain a feature sampling sequence; Subtract the previous sampling point data from the next sampling point data in the feature sampling sequence, where the first sampling point data corresponds to the subtraction of the last sampling point data in the previous feature sampling sequence, to obtain the feature fluctuation sequence; Calculate the number of fluctuations and the fluctuation value in the characteristic fluctuation sequence. If multiple consecutive feature points in the characteristic fluctuation sequence have the same direction, it is marked as a fluctuation, and the fluctuation value is the sum of the values ​​of the multiple feature points. Each fluctuation value is compared with a second preset threshold. If the fluctuation value is greater than the second preset threshold, the fluctuation is marked as a valid fluctuation. The number of valid fluctuations in the characteristic fluctuation sequence is counted. If the number of valid fluctuations is greater than a third preset threshold, the characteristic fluctuation sequence is determined to be a characteristic signal waveform.

2. The household-transformer relationship identification method based on feature signal waveforms as described in claim 1, characterized in that, The process of determining the feature code of the signal waveform within the sliding window after each sliding also includes the following: Record the maximum fluctuation value in the characteristic fluctuation sequence, and calculate the average value based on the maximum fluctuation values ​​corresponding to multiple characteristic fluctuation sequences within the sliding window to obtain the amplitude of the characteristic current signal within the sliding window.

3. The household-transformer relationship identification method based on feature signal waveforms as described in claim 1, characterized in that, If the characteristic fluctuation sequence is determined to be a non-characteristic signal waveform, the characteristic fluctuation sequence is sorted from largest to smallest. If the first 1 / 4 digits are less than the fourth preset threshold, the current characteristic fluctuation sequence is updated to a power frequency data sequence without characteristic current signal.

4. The household-transformer relationship identification method based on feature signal waveforms as described in claim 1, characterized in that, If four consecutive feature codes are all 1 when the sliding window slides, the sampling point data sequence of any one cycle within the four sliding windows will be updated to a power frequency data sequence without feature current signals.

5. The household-transformer relationship identification method based on characteristic signal waveforms as described in any one of claims 1 to 4, characterized in that, After obtaining the initial data sequence, the following content is also included: Linear interpolation is performed on each cycle of the initial data sequence to obtain the interpolation sequence of each cycle, wherein the number of interpolations between any two adjacent data points is the same. The data point that first crosses zero is found from the interpolation sequence of each cycle, and points are selected at equal intervals based on the position of the data point that first crosses zero and the number of interpolations between any two adjacent data points to obtain the preprocessed data sequence of each cycle.

6. A household-transformer relationship identification system based on characteristic signal waveforms, employing the household-transformer relationship identification method based on characteristic signal waveforms as described in any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to acquire an initial data sequence containing characteristic current signals; The window construction module is used to construct a sliding window, the length of which is not less than the duration of the periodic change of the characteristic current signal; The intelligent recognition module is used to slide a sliding window in the initial data sequence. The sliding step size is the duration of the periodic change of the characteristic current signal. After each slide, the signal waveform in the sliding window is judged by the feature code. If multiple feature codes are obtained in succession and the constructed feature signal is the same as the characteristic current signal, the household-transformation relationship is obtained.

7. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium for storing a computer program for identifying household-transformation relationships based on characteristic signal waveforms, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 5.

Images