One-address multi-user identification method and system for electric power

Abstract

The invention relates to the technical field of power management, in particular to a one-address multi-user identification method and system for power, and the method comprises the following steps: installing a current transformer at an access point of a main wire, collecting the current flowing data on a bus in real time, recording the current waveform and amplitude, and generating current monitoring data. According to the invention, the current transformer is installed at the access point of the main electric wire to realize real-time collection of current data, ensure continuous updating of the data flow and improve the timeliness and accuracy of the data, and the current waveform and amplitude are analyzed through the frequency spectrum and are matched with the user electric power mode, so that each identified load electric power mode is effectively distinguished; the electric power monitoring precision is greatly improved, the acoustic sensor is used for capturing acoustic waves generated by current flowing, extracting key features such as frequency and amplitude, integrating synchronous load data and then performing comparative analysis, so that accurate identification of electric power households, optimization of resource allocation and improvement of fairness and management efficiency of electric power supply can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a method and system for identifying multiple households using a single address for power. Background Art

[0002] The field of power management technology involves the use of various tools and methods to monitor and control power production, distribution, and consumption to improve energy efficiency, ensure the stable operation of the power system, and optimize resource allocation. It includes smart grid technology, load management, power system monitoring, and demand response strategies. Smart meters and home automation systems are also part of power management technology, helping users and power companies monitor and adjust power use in real time. In addition, power management also includes the use of advanced algorithms and information technology to predict power demand, optimize power generation, and reduce energy waste to achieve a more economical and environmentally friendly power system.

[0003] Among them, the method of identifying multiple households at one electricity address is a specialized application in the field of power management technology. It is mainly used to identify the power usage of multiple households or users at the same power address, which can ensure the accuracy of power billing, prevent the abuse of power resources, and improve the management efficiency of power supply. By accurately distinguishing the power usage of each householder, power costs can be distributed more fairly, while helping power suppliers to better understand and manage power demand. This is particularly important in multi-family homes, commercial buildings, and industrial parks, and can support more complex power management and billing strategies.

[0004] Existing methods lack the means to analyze the power behavior of users in detail in terms of multi-household power identification, which limits the accuracy and fairness of power billing. For example, when the power usage of each household is not accurately identified, some users are charged too high or too low, causing user dissatisfaction and economic disputes. In addition, the lack of effective load feature identification technology makes it difficult to intervene in power abuse in a timely manner, increasing the management cost and operation risk of the power system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for identifying multiple households at one electricity address.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for identifying multiple households using one address for electricity, comprising the following steps:

[0007] S1: Install current transformers at the access point of the main power line to collect current flow data on the bus in real time, record current waveform and amplitude, and generate current monitoring data;

[0008] S2: The host performs spectrum analysis on the current monitoring data, detects the user's power mode through the neutral and live wire connections, matches the current waveform and load characteristics, and distinguishes and marks each identified load power mode to generate a load feature file;

[0009] S3: The slave receives the load characteristic file from the master, uploads the load power usage data at the user end through the network, verifies and synchronizes the data in real time, and generates synchronized load data;

[0010] S4: Use an acoustic sensor to capture the sound waves generated by the flow of current, perform fast Fourier transform on the sound waves, analyze the frequency and amplitude of the sound wave data, extract key sound wave features, and generate sound wave feature data;

[0011] S5: Integrate the acoustic wave characteristic data and the synchronous load data, perform comparative analysis, match the frequency and amplitude with the actual power user, confirm the power usage behavior of the actual power user through pattern matching, and generate a power household identification result.

[0012] As a further solution of the present invention, the current monitoring data includes current frequency, current phase and current amplitude; the load characteristic file includes power mode identification, power mode difference mark and power mode continuity analysis results; the synchronous load data includes load consumption mode, synchronization timestamp and data integrity status record; the sound wave characteristic data includes sound wave frequency distribution record, sound wave amplitude level and sound wave duration; the electricity household identification results include household usage frequency, household power mode and power behavior consistency analysis results.

[0013] As a further solution of the present invention, a current transformer is installed at the access point of the main power line to collect the current flow data on the bus in real time, record the current waveform and amplitude, and generate the current monitoring data in the following specific steps:

[0014] S101: Install a current transformer at the main power line access point, connect the current transformer output end to the data acquisition unit interface through a cable, check the electrical connection stability, and generate a record of the equipment installation status;

[0015] S102: Based on the equipment installation status record, set the data sampling frequency and recording interval, detect the acquisition accuracy by inputting the test current and adjust the recording range, monitor the bus current waveform and amplitude in real time, and generate real-time monitoring data;

[0016] S103: Based on the real-time monitoring data, extract the current record by reading the data buffer area, perform timestamp matching to check the integrity of the acquisition sequence, organize the current waveform and amplitude data according to the time series format, and generate current monitoring data.

[0017] As a further solution of the present invention, the host performs spectrum analysis on the current monitoring data, detects the power mode of the user through the neutral and live wire connection, matches the current waveform and the load characteristics, and distinguishes and marks each identified load power mode. The specific steps of generating a load feature file are as follows:

[0018] S201: extracting bus current waveform data based on the current monitoring data, segmenting the data to distinguish current signals in continuous time periods, recording frequency component amplitude and phase information, and generating current spectrum distribution records;

[0019] S202: Based on the current spectrum distribution record, the power usage pattern is obtained through zero-live signal detection, the spectrum characteristics are matched with the power usage pattern item by item, the similarity between the waveforms is calculated, and the load spectrum and pattern matching information is generated;

[0020] S203: Based on the load spectrum and pattern matching information, classification is performed through grouping tags, differences between spectrum distributions are compared to distinguish load characteristics, and spectrum characteristic labels of each group of loads are added to generate a load characteristic file.

[0021] As a further solution of the present invention, the similarity between the waveforms is calculated according to the formula:

[0022]

[0023] Calculate, where similarity represents the similarity value between waveforms, X k Represents the intensity of the first waveform at frequency point k, Y k Represents the intensity of the second waveform at frequency point k, where K represents the total number of frequency points.

[0024] As a further solution of the present invention, the slave receives the load characteristic file of the master, uploads the load power usage data at the user end through the network, verifies and synchronizes the data in real time, and generates synchronized load data in the following specific steps:

[0025] S301: Based on the load characteristic file, establish a network connection channel, receive data packets and verify data integrity, parse the load characteristic data field by field and store it in the slave buffer area, and generate local load characteristic data;

[0026] S302: extracting real-time load power usage data based on the local load characteristic data, performing field comparison check, marking data verification status through difference detection, and generating verified user load data;

[0027] S303: Based on the verified user load data, the data is packaged and uploaded to the server, the response status of the upload process is verified and the timestamp of the synchronization completion is recorded to generate the synchronization load data.

[0028] As a further solution of the present invention, the acoustic sensor is used to capture the sound waves generated by the current flow, the sound waves are subjected to fast Fourier transform, the frequency and amplitude of the sound wave data are analyzed, and the key sound wave features are extracted. The specific steps of generating the sound wave feature data are as follows:

[0029] S401: using an acoustic sensor to capture the sound waves generated by the current flow, converting the analog sound wave signal into a digital signal, removing background noise through a filter and calibrating the signal amplitude, performing time domain sampling and recording, and generating sound wave digital signal data;

[0030] S402: Based on the sound wave digital signal data, the continuous signal is divided into fixed time periods, the frequency amplitude of each sound wave signal is collected, the frequency component and amplitude information are summarized and counted, and the sound wave frequency amplitude data is generated;

[0031] S403: Based on the sound wave frequency and amplitude data, analyze the frequency and amplitude sequence to extract key sound wave features, mark the key sound wave features according to the frequency range and amplitude threshold, and generate sound wave feature data.

[0032] As a further solution of the present invention, the acoustic wave characteristic data and the synchronous load data are integrated for comparative analysis, the frequency and amplitude are matched with the actual power user, the power usage behavior of the actual power user is confirmed by pattern matching, and the specific steps of generating the power household identification result are as follows:

[0033] S501: Integrate the acoustic wave feature data and the synchronous load data, read the timestamps of the two sets of data, perform time series matching and align data segments, analyze the frequency and amplitude change relationship between the acoustic wave data and the load data, and generate a matching result of the acoustic wave and load data;

[0034] S502: based on the sound wave and load data matching result, extract the frequency and amplitude characteristics of the sound wave and the load corresponding segment, calculate the characteristic difference matching degree, identify the load correlation of the sound wave characteristics, and generate the characteristic correlation data matching result;

[0035] S503: Based on the feature-related data matching results, the corresponding relationship between the acoustic wave features and the load features is analyzed, the power consumption patterns of the power users are classified, the power consumption behavior characteristics of each type of user are extracted, the data and classification information are sorted, and the power household identification results are generated.

[0036] As a further solution of the present invention, the characteristic difference matching degree is calculated according to the formula:

[0037]

[0038] Calculate, where M represents the matching value of the characteristic difference between the acoustic wave feature and the load feature, and Fs,i Represents the frequency or amplitude value of the sound wave characteristic of the i-th segment, F l,i Represents the frequency or amplitude value of the i-th load characteristic, and n is the total number of characteristic sections.

[0039] A system for identifying multiple households using one address for electricity, comprising:

[0040] The current monitoring module installs a current transformer to collect current, sets the data sampling frequency and recording interval, monitors the bus current waveform and amplitude in real time, organizes the current waveform and amplitude data in a time series format, and generates current monitoring data;

[0041] The pattern matching module performs data segmentation based on the current monitoring data to distinguish the current signal in a continuous time period, matches the spectrum characteristics with the power usage pattern item by item, calculates the similarity between the waveforms, and generates load spectrum and pattern matching information;

[0042] The feature classification module classifies the load spectrum and pattern matching information through grouping tags, compares the differences between spectrum distributions to distinguish load characteristics, parses the data field by field and stores it in the slave cache area to generate local load feature data;

[0043] The data verification module performs field comparison check based on the local load characteristic data, marks the data verification status and uploads it to the server, verifies the response status of the upload process and records the timestamp of synchronization completion, and generates synchronized load data;

[0044] The acoustic wave analysis module uses an acoustic sensor to capture the acoustic waves generated by the flow of current, performs time domain sampling and recording, divides the continuous signal into fixed time periods, collects the frequency amplitude of each acoustic wave signal, analyzes the frequency and amplitude sequence to extract key acoustic wave features, and generates acoustic wave feature data;

[0045] The user identification module integrates the acoustic wave feature data and the synchronous load data, analyzes the frequency and amplitude change relationship between the acoustic wave data and the load data, calculates the characteristic difference matching degree, identifies the load correlation of the acoustic wave feature, classifies the power consumption pattern of the power users, extracts the power consumption behavior characteristics of each type of user, and generates the power household identification result.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, current transformers are installed at the access points of the main power lines to realize real-time collection of current data, ensure continuous updating of data streams, improve data timeliness and accuracy, analyze current waveforms and amplitudes through spectrum analysis, and match them with user power patterns, so that each identified load power pattern can be effectively distinguished, greatly improving the accuracy of power monitoring, and using acoustic sensors to capture sound waves generated by current flow, and extract key features such as frequency and amplitude. After integrating synchronous load data, comparative analysis can achieve accurate identification of power households, optimize resource allocation, and improve the fairness and management efficiency of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the steps of the present invention;

[0049] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0050] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0051] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0052] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0053] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0054] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0057] See also Figure 1A method for identifying multiple households using one address for electricity comprises the following steps:

[0058] S1: Install current transformers at the access point of the main power line to collect current flow data on the bus in real time, record current waveform and amplitude, and generate current monitoring data;

[0059] S2: The host performs spectrum analysis on the current monitoring data, detects the user's power mode through the neutral and live wire connections, matches the current waveform and load characteristics, and distinguishes and marks each identified load power mode to generate a load feature file;

[0060] S3: The slave receives the load characteristic file from the master, uploads the load power usage data at the user end through the network, verifies and synchronizes the data in real time, and generates synchronized load data;

[0061] S4: Use an acoustic sensor to capture the sound waves generated by the flow of current, perform fast Fourier transform on the sound waves, analyze the frequency and amplitude of the sound wave data, extract key sound wave features, and generate sound wave feature data;

[0062] S5: Integrate the acoustic wave feature data and the synchronous load data, conduct comparative analysis, match the frequency and amplitude with the actual power users, confirm the power usage behavior of the actual power users through pattern matching, and generate power household identification results.

[0063] Current monitoring data includes current frequency, current phase and current amplitude; load characteristic files include power mode identification, power mode difference mark and power mode continuity analysis results; synchronous load data includes load consumption mode, synchronization timestamp and data integrity status record; acoustic wave characteristic data includes acoustic wave frequency distribution record, acoustic wave amplitude level and acoustic wave duration; power household identification results include household usage frequency, household power mode and power behavior consistency analysis results.

[0064] See also Figure 2 , the specific steps of S1 are:

[0065] S101: Install a current transformer at the main power line access point, connect the current transformer output end to the data acquisition unit interface through a cable, check the electrical connection stability, and generate a record of the equipment installation status;

[0066] Install current transformers at the access point of the main power line. The selected transformers must be based on the rated current value and operating frequency range of the line. The adapter model is determined through specification analysis to ensure that the real-time current of the busbar can be accurately sensed. Use an insulated sheathed cable to reliably connect the output end of the transformer to the interface of the data acquisition unit. Use a torque wrench to apply standard torque to all electrical connection parts in accordance with technical specifications. At the same time, use a continuity tester to test the connection circuits one by one to ensure that the contact resistance is less than 1 milliohm and the contacts are stable and reliable. Then use an insulation tester to verify the insulation performance of the installed line to ensure that there is no leakage problem under high load conditions. Finally, the specific location, model and connection test results of the equipment installation are recorded through a recorder, and the equipment installation status record is generated and archived.

[0067] S102: Based on the equipment installation status record, set the data sampling frequency and recording interval, detect the acquisition accuracy by inputting the test current and adjust the recording range, monitor the bus current waveform and amplitude in real time, and generate real-time monitoring data;

[0068] In the above content, the data sampling frequency and recording interval are set, and the bus current waveform and amplitude are monitored in real time. According to the formula

[0069]

[0070] Calculate the effective value of the bus current. In the formula, I(t) represents the effective value of the bus current at a certain moment, T represents the sampling time period, i(τ) represents the instantaneous current value, and τ is the time variable. The instantaneous bus current i(τ) is obtained in real time through the sampling device, using a sampling frequency of 500 Hz and a sampling time period of T = 0.02 seconds. The square values ​​of the instantaneous current in each cycle are accumulated and averaged, and finally the square root of the result is taken to obtain the effective value. For example, the sampling data for a certain period of time is i(τ) = [2.0, 2.2, 2.1, 2.0, 2.3] amperes, and the accumulated square is:

[0071] ∑i 2 (τ)=4.0+4.84+4.41+4.0+5.29=22.54

[0072] The average value is:

[0073]

[0074] Taking the square root gives:

[0075] ampere

[0076] This effective value indicates the operating current status of the bus and is used for monitoring and abnormality analysis.

[0077] S103: Based on the real-time monitoring data, extract the current record by reading the data buffer area, perform timestamp matching to check the integrity of the acquisition sequence, organize the current waveform and amplitude data according to the time series format, and generate current monitoring data;

[0078] Based on real-time monitoring data, the control program calls the reading module of the data buffer area to extract each data record in turn, including the timestamp and the corresponding current value. The verification algorithm is used to detect the continuity and integrity of the record. By checking the acquisition intervals of different time tags, possible duplicate or erroneous tags are eliminated. All time tags are arranged in ascending order, and the corresponding current values ​​are matched to form a standardized time series. At the same time, the extracted amplitude data is reconstructed, including signal smoothing and data interpolation to reduce breakpoint errors. Finally, the reconstructed current waveform data is stored in the current monitoring database to ensure that the generated current monitoring data can accurately reflect the operation of the busbar.

[0079] See also Figure 3 , the specific steps of S2 are:

[0080] S201: extract bus current waveform data based on current monitoring data, segment and distinguish current signals in continuous time periods, record frequency component amplitude and phase information, and generate current spectrum distribution records;

[0081] Based on the current monitoring data, the waveform information of the bus current is obtained by data reading, and the monitoring data is segmented by a time series segmentation algorithm. Each segment of data is divided into several sub-intervals according to its time continuity and current amplitude change characteristics. The data of each sub-interval is transformed into the frequency domain using the fast Fourier transform, and the amplitude and phase information of the frequency component are extracted. The amplitude is standardized to ensure the consistency of the data. The amplitude and phase of each frequency component are marked by data matching, and the frequency components are sorted and recorded in increasing frequency. In order to improve the accuracy of the spectrum distribution record, noise components and abnormal data points are eliminated during the data processing process, and the signal quality is optimized using filtering technology. The final output current spectrum distribution record contains complete time tags, frequency component amplitude and phase data, which provides detailed frequency domain information support for the subsequent analysis of power usage patterns.

[0082] S202: Based on the current spectrum distribution record, the power usage pattern is obtained through zero-live signal detection, the spectrum characteristics are matched with the power usage pattern item by item, the similarity between the waveforms is calculated, and the load spectrum and pattern matching information is generated;

[0083] The similarity between waveforms is based on the formula:

[0084]

[0085] Calculate, where similarity represents the similarity value between waveforms, X k represents the intensity of the first waveform at frequency point k, the current intensity at a specific frequency point obtained from the first power device or mode, Y k Represents the intensity of the second waveform at frequency point k, the current intensity of the corresponding frequency point obtained from the second power device or mode, and K represents the total number of frequency points.

[0086] Through the detection of zero-line and live-line signals, the current spectrum data of the two power usage modes are obtained. In the spectrum analysis, K=5 key frequency points are selected to obtain the following data:

[0087] Frequency point k <![CDATA[X k (Mode 1 Intensity)]]> <![CDATA[Y k (Mode 2 Intensity)]]> 1 10 12 2 15 15 3 20 18 4 25 22 5 30 28

[0088] calculate

[0089] (10×12)+(15×15)+(20×18)+(25×22)+(30×28)

[0090] =120+225+360+550+840=2095

[0091] calculate and

[0092]

[0093] calculate

[0094]

[0095] calculate

[0096]

[0097] reciprocal:

[0098]

[0099] Calculate similarity:

[0100] similarity=0.9978+0.1792=1.177

[0101] The results show that the current spectra of the two power usage patterns are highly similar at the selected frequency points, and the comprehensive similarity score is 1.177, which is close to the theoretical maximum value, indicating that the waveform characteristics of the two are very close.

[0102] S203: Based on the load spectrum and pattern matching information, classify through grouping tags, compare the differences between spectrum distributions to distinguish load characteristics, and add spectrum characteristic labels for each group of loads to generate a load characteristic file;

[0103] Based on the load spectrum and pattern matching information, different spectrum matching records are grouped, and the clustering algorithm is used to classify the data according to the spectrum amplitude and phase characteristics. The characteristic range of each group is determined by calculating the mean, variance and peak characteristics of the frequency components. The statistical characteristics of the frequency components are stored corresponding to the labels of each group. Then, the spectrum analysis tool is used to detect the consistency of the characteristics within the group, and the frequency components that deviate from the group mean range are reclassified or eliminated. According to the analysis results, the spectrum characteristic labels of each group of loads are generated, including parameters such as frequency range, main component amplitude, phase distribution, etc. All classification information is summarized and stored as a load characteristic file. The integrity of the file content is verified by the verification tool to ensure that the description of each group of load characteristics is accurate and can intuitively reflect the differences in load characteristics, providing detailed and reliable classification basic data for subsequent analysis.

[0104] See also Figure 4 , the specific steps of S3 are:

[0105] S301: Based on the load characteristic file, a network connection channel is established, data packets are received and data integrity is verified, load characteristic data is parsed field by field and stored in a slave buffer area, and local load characteristic data is generated;

[0106] Based on the load characteristic file, the network communication module is started to establish a network connection channel with the slave machine, and the TCP / IP protocol is used to ensure the stability of transmission. The load characteristic data packet is parsed, and the integrity of the data packet is checked one by one, including the packet header information verification and the check code comparison. The part that fails the check or is lost is requested to be retransmitted to ensure that the received data content is consistent with the source data. Then the load characteristic data is parsed field by field, and the parsing results are organized into structured data according to the preset format. The structured data is stored in the slave cache area, and the timestamp of each data reception is recorded. Finally, the local load characteristic data is generated. The data contains complete field information and parsing records to provide support for subsequent power usage analysis.

[0107] S302: extracting real-time load power usage data based on local load feature data, performing field comparison checks, marking data verification status through difference detection, and generating verified user load data;

[0108] Based on the local load characteristic data, the real-time load power usage data is obtained, and the corresponding field values ​​of the real-time data and the local characteristic data are compared field by field. The numerical difference is detected by calculating the field difference, and the data abnormal state is marked according to the difference exceeding the preset threshold. For the data field marked with abnormality, its context field relevance is further analyzed to check whether there is batch deviation or single point abnormality, and the data verification status is comprehensively judged whether it has passed. After the data verification is completed, all the data field values ​​and abnormal marking status that have passed the verification are recorded, and the verified user load data is generated for subsequent usage pattern statistics and optimization analysis.

[0109] S303: Based on the verified user load data, the data is packaged and uploaded to the server, the response status of the upload process is verified and the timestamp of the synchronization completion is recorded to generate the synchronization load data;

[0110] Based on the verified user load data, a standardized data packet is generated according to the server's upload protocol, and the data packet is uploaded to the server through an encrypted transmission channel. Each response status during the upload process is detected, including indicators such as upload delay, packet reception confirmation, and integrity verification, to ensure that the uploaded data packet has no errors. For each successfully uploaded data packet, the timestamp and response status information of the upload completion are recorded to generate synchronous load data, which contains complete user load usage information and upload logs, providing a time stamp and data synchronization basis for further processing on the server side, and a highly reliable data source for remote analysis.

[0111] See also Figure 5 , the specific steps of S4 are:

[0112] S401: using an acoustic sensor to capture the sound waves generated by the current flow, converting the analog sound wave signal into a digital signal, removing background noise through a filter and calibrating the signal amplitude, performing time domain sampling and recording, and generating sound wave digital signal data;

[0113] Acoustic sensors are used to capture the acoustic wave signals generated during the flow of current. The acoustic wave signals are converted into electrical signals through the analog circuit of the sensor, and then the electrical signals are converted into digital signals using an analog-to-digital converter. The digital signals are processed using a bandpass filter to filter out background noise and invalid frequency components below 20Hz and above 20kHz, retaining the frequency range related to the flow of current. The signal amplitude is calibrated to compensate for the sensitivity deviation of the sensor to ensure that the signal amplitude can truly reflect the intensity of the sound wave. The filtered and calibrated signal is then sampled in the time domain with a sampling frequency set to 44.1kHz. The sampled signal data is recorded in chronological order, and finally a complete digital acoustic signal data is generated, providing a data basis for subsequent frequency domain and feature analysis.

[0114] S402: Based on the sound wave digital signal data, the continuous signal is divided into fixed time periods, the frequency amplitude of each sound wave signal is collected, the frequency component and amplitude information are summarized and counted, and the sound wave frequency amplitude data is generated;

[0115] Based on the sound wave digital signal data, the continuous sound wave signal is divided into fixed time periods. According to the duration of each signal segment, all key sound wave changes are covered. Then the frequency component and corresponding amplitude of each sound wave signal segment are calculated. The frequency value and amplitude of each frequency component are obtained by fast Fourier transform, and all segment results are summarized and statistically analyzed to generate the frequency component distribution and amplitude characteristics of each segment. The amplitude information of the frequency component is normalized to avoid the amplitude being misleading due to the difference in absolute signal strength. The statistical results are sorted according to time sequence and segment number, and finally the sound wave frequency amplitude data is formed, which provides accurate frequency and amplitude distribution basis for the subsequent key sound wave feature extraction.

[0116] S403: Based on the sound wave frequency and amplitude data, analyze the frequency and amplitude sequence to extract key sound wave features, mark the key sound wave features according to the frequency range and amplitude threshold, and generate sound wave feature data;

[0117] Based on the sound wave frequency and amplitude data, the change pattern of the frequency and amplitude sequence is analyzed, and the key frequency and corresponding amplitude information are screened out by setting the frequency range and amplitude threshold, and the sound wave frequency band containing the characteristic signal is identified. The main frequency, amplitude mean and maximum amplitude of the screened key sound wave frequency band are calculated using the frequency statistics method, and the key feature points in each sound wave are marked and the corresponding time index is recorded. At the same time, the correlation analysis between amplitude and frequency is used to exclude possible invalid features. Finally, all feature information is organized into sound wave feature data, including the frequency range, amplitude characteristics and time mark of each key sound wave, providing complete feature basic data for subsequent sound wave pattern matching and analysis.

[0118] See also Figure 6 , the specific steps of S5 are:

[0119] S501: Integrate the acoustic wave feature data and the synchronous load data, read the timestamps of the two sets of data, perform time series matching and align the data segments, analyze the frequency and amplitude change relationship between the acoustic wave data and the load data, and generate the acoustic wave and load data matching results;

[0120] Integrate the acoustic wave feature data and the synchronous load data, extract the time tag information of the two sets of data respectively, compare the time range of the data, and align the acoustic wave data and load data in the overlapping time period. According to the matched data segments, analyze the correlation between the frequency and amplitude information of the acoustic wave data and the frequency and amplitude changes of the load data, and use the correlation coefficient and linear regression model to calculate the response amplitude of the acoustic wave frequency change to the load frequency fluctuation. Record the corresponding relationship in each time period during the analysis process, and organize these relationships into matching results output, forming a matching result of acoustic wave and load data containing time index, acoustic wave characteristics, load characteristics and correlation analysis indicators.

[0121] S502: based on the sound wave and load data matching results, extract the frequency and amplitude characteristics of the sound wave and the load corresponding segment, calculate the characteristic difference matching degree, identify the load correlation of the sound wave characteristics, and generate the characteristic correlation data matching results;

[0122] The characteristic difference matching degree is according to the formula:

[0123]

[0124] Calculate, where M represents the matching value of the characteristic difference between the acoustic wave feature and the load feature, and F s,i Represents the frequency or amplitude value of the sound wave characteristic of the i-th segment, F l,i Represents the frequency or amplitude value of the i-th load characteristic, and n is the total number of characteristic sections.

[0125] The acoustic wave signal and load data are divided into three time periods respectively, and the extracted eigenvalues ​​are as follows:

[0126] Sound wave characteristics: F s,1 =2.0, F s,2 =3.5, F s,3 =4.0.

[0127] Load characteristics: F l,1 =1.5, F l,2 =3.0, F l,3 =4.5.

[0128] Calculate the numerator part:

[0129]

[0130] Calculate the denominator:

[0131]

[0132] Calculate the matching degree:

[0133]

[0134] The results show that the matching degree of the characteristic difference between the acoustic wave characteristics and the load characteristics is 0.094. The smaller the value, the closer the characteristics of the two are.

[0135] S503: Based on the feature association data matching result, the corresponding relationship between the acoustic wave feature and the load feature is analyzed, the power consumption mode of the power users is classified, the power consumption behavior characteristics of each type of user are extracted, the data and classification information are sorted, and the power household identification result is generated;

[0136] Based on the feature association data matching results, the power consumption patterns of different types of users are analyzed through the correlation between load characteristics and acoustic wave characteristics, and the power consumption behavior characteristics of each type of user are extracted, including the frequency characteristics of load operation, power distribution and acoustic wave characteristic range. The users are classified and analyzed, and the power consumption behavior characteristics of the same type of users are classified into a unified category. Typical behavior characteristics, such as load startup frequency, operation duration, etc., are extracted. The power consumption data of all classified users and their corresponding behavior characteristics are sorted out, and combined with the classification labels, the output is the power household identification result. The result contains user classification information and its main power consumption pattern characteristics, providing a clear user classification basis and reference for power supply management.

[0137] See also Figure 7 , a system for identifying multiple households using one address for electricity, comprising:

[0138] The current monitoring module installs a current transformer to collect current, sets the data sampling frequency and recording interval, monitors the bus current waveform and amplitude in real time, organizes the current waveform and amplitude data in a time series format, and generates current monitoring data;

[0139] The pattern matching module divides the data into segments based on the current monitoring data to distinguish the current signals in continuous time periods, matches the spectrum characteristics with the power usage pattern item by item, calculates the similarity between the waveforms, and generates the load spectrum and pattern matching information;

[0140] The feature classification module classifies the load spectrum and pattern matching information through grouping tags, compares the differences between spectrum distributions to distinguish load characteristics, parses the data field by field and stores it in the slave cache to generate local load feature data;

[0141] The data verification module performs field comparison checks based on local load feature data, marks the data verification status and uploads it to the server, verifies the response status of the upload process and records the timestamp of synchronization completion, and generates synchronized load data;

[0142] The acoustic wave analysis module uses an acoustic sensor to capture the acoustic waves generated by the flow of current, performs time domain sampling and recording, divides the continuous signal into fixed time periods, collects the frequency amplitude of each acoustic wave signal, analyzes the frequency and amplitude sequence to extract key acoustic wave features, and generates acoustic wave feature data;

[0143] The user identification module integrates the acoustic wave feature data and the synchronous load data, analyzes the frequency and amplitude change relationship between the acoustic wave data and the load data, calculates the matching degree of the characteristic difference, identifies the load correlation of the acoustic wave feature, classifies the power consumption patterns of the power users, extracts the power consumption behavior characteristics of each type of user, and generates the power household identification results.

[0144] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for identifying multiple households using one address for electricity, characterized in that: The following steps are involved: Install current transformers at the access points of the main power lines to collect current flow data on the busbars in real time, record current waveforms and amplitudes, and generate current monitoring data; The host performs spectrum analysis on the current monitoring data, detects the user's power mode through the neutral and live wire connections, matches the current waveform and load characteristics, and distinguishes and marks each identified load power mode to generate a load feature file; The slave receives the load characteristic file from the master, uploads the load power usage data at the user end through the network, verifies and synchronizes the data in real time, and generates synchronized load data; Use acoustic sensors to capture the sound waves generated by the flow of current, perform fast Fourier transform on the sound waves, analyze the frequency and amplitude of the sound wave data, extract key sound wave features, and generate sound wave feature data; The acoustic wave characteristic data and the synchronous load data are integrated for comparative analysis, the frequency and amplitude are matched with the actual power user, the power usage behavior of the actual power user is confirmed through pattern matching, and the power household identification result is generated.

2. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: The current monitoring data includes current frequency, current phase and current amplitude; the load characteristic file includes power mode identification, power mode difference mark and power mode continuity analysis results; the synchronous load data includes load consumption mode, synchronization timestamp and data integrity status record; the acoustic wave characteristic data includes acoustic wave frequency distribution record, acoustic wave amplitude level and acoustic wave duration; the electricity household identification results include household usage frequency, household power mode and power behavior consistency analysis results.

3. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: Install current transformers at the access point of the main power line, collect current flow data on the bus in real time, record current waveform and amplitude, and generate current monitoring data in the following specific steps: Install the current transformer at the main power line access point, connect the current transformer output to the data acquisition unit interface through a cable, check the electrical connection stability, and generate a record of the equipment installation status; Based on the equipment installation status record, the data sampling frequency and recording interval are set, the acquisition accuracy is detected by inputting the test current and the recording range is adjusted, the bus current waveform and amplitude are monitored in real time, and real-time monitoring data is generated; Based on the real-time monitoring data, the current record is extracted by reading the data buffer area, the time stamp matching is performed to check the integrity of the acquisition sequence, and the current waveform and amplitude data are sorted according to the time series format to generate current monitoring data.

4. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: The host performs spectrum analysis on the current monitoring data, detects the user's power mode through the neutral and live wire connection, matches the current waveform and load characteristics, and distinguishes and marks each identified load power mode. The specific steps for generating a load feature file are as follows: Based on the current monitoring data, bus current waveform data is extracted, data segmentation is performed to distinguish current signals in continuous time periods, frequency component amplitude and phase information is recorded, and current spectrum distribution records are generated; Based on the current spectrum distribution record, the power usage pattern is obtained through zero-live signal detection, the spectrum characteristics are matched with the power usage pattern item by item, the similarity between the waveforms is calculated, and the load spectrum and pattern matching information is generated; Based on the load spectrum and pattern matching information, classification is performed through grouping tags, the differences between spectrum distributions are compared to distinguish load characteristics, and spectrum characteristic labels of each group of loads are added to generate a load feature file.

5. The method for identifying multiple households using one address for electricity according to claim 4, characterized in that: The similarity between the waveforms is based on the formula: Calculate, where similarity represents the similarity value between waveforms, X k Represents the intensity of the first waveform at frequency point k, Y k Represents the intensity of the second waveform at frequency point k, where K represents the total number of frequency points.

6. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: The slave receives the load characteristic file from the master, uploads the load power usage data at the user end through the network, verifies and synchronizes the data in real time, and generates synchronized load data in the following specific steps: Based on the load characteristic file, a network connection channel is established to receive data packets and verify data integrity, load characteristic data is parsed field by field and stored in a slave buffer area to generate local load characteristic data; Based on the local load characteristic data, extract the real-time load power usage data, perform field comparison check, mark the data verification status through difference detection, and generate verified user load data; Based on the verified user load data, the data is packaged and uploaded to the server, the response status of the upload process is verified and the timestamp of the synchronization completion is recorded to generate the synchronized load data.

7. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: The specific steps of using acoustic sensors to capture the sound waves generated by the flow of current, performing fast Fourier transform on the sound waves, analyzing the frequency and amplitude of the sound wave data, and extracting key sound wave features to generate sound wave feature data are as follows: Use acoustic sensors to capture the sound waves generated by the flow of current, convert the analog sound wave signals into digital signals, remove background noise through filters and calibrate the signal amplitude, perform time domain sampling and recording, and generate sound wave digital signal data; Based on the sound wave digital signal data, the continuous signal is divided into fixed time periods, the frequency amplitude of each sound wave signal is collected, the frequency component and amplitude information are summarized and counted, and the sound wave frequency amplitude data is generated; Based on the sound wave frequency and amplitude data, the frequency and amplitude sequences are analyzed to extract key sound wave features, the key sound wave features are marked according to the frequency range and amplitude threshold, and the sound wave feature data is generated.

8. The method for identifying multiple households using one address for electricity according to claim 1, characterized in that: The specific steps of integrating the acoustic wave characteristic data and the synchronous load data, performing comparative analysis, matching the frequency and amplitude with the actual power user, confirming the power usage behavior of the actual power user through pattern matching, and generating the power household identification result are as follows: Integrate the acoustic wave characteristic data and the synchronous load data, read the timestamps of the two sets of data, perform time series matching and align data segments, analyze the frequency and amplitude change relationship between the acoustic wave data and the load data, and generate the acoustic wave and load data matching results; Based on the sound wave and load data matching results, frequency and amplitude characteristics of the sound wave and load corresponding segments are extracted, the characteristic difference matching degree is calculated, the load correlation of the sound wave characteristics is identified, and the characteristic correlation data matching results are generated; Based on the feature-related data matching results, the correspondence between the acoustic wave features and the load features is analyzed, the power consumption patterns of power users are classified, the power consumption behavior characteristics of each type of user are extracted, the data and classification information are sorted out, and the power household identification results are generated.

9. The method for identifying multiple households using one address for electricity according to claim 8, characterized in that: The characteristic difference matching degree is according to the formula: Calculate, where M represents the matching value of the characteristic difference between the acoustic wave feature and the load feature, and F s,i Represents the frequency or amplitude value of the sound wave characteristic of the i-th segment, F l,i Represents the frequency or amplitude value of the i-th load characteristic, and n is the total number of characteristic sections.

10. A system for identifying multiple households using one address for electricity, characterized in that: According to the method for identifying multiple households using one address for electricity according to any one of claims 1 to 9, the system comprises: The current monitoring module installs a current transformer to collect current, sets the data sampling frequency and recording interval, monitors the bus current waveform and amplitude in real time, organizes the current waveform and amplitude data in a time series format, and generates current monitoring data; The pattern matching module performs data segmentation based on the current monitoring data to distinguish the current signal in a continuous time period, matches the spectrum characteristics with the power usage pattern item by item, calculates the similarity between the waveforms, and generates load spectrum and pattern matching information; The feature classification module classifies the load spectrum and pattern matching information through grouping tags, compares the differences between spectrum distributions to distinguish load characteristics, parses the data field by field and stores it in the slave cache area to generate local load feature data; The data verification module performs field comparison check based on the local load characteristic data, marks the data verification status and uploads it to the server, verifies the response status of the upload process and records the timestamp of synchronization completion, and generates synchronized load data; The acoustic wave analysis module uses an acoustic sensor to capture the acoustic waves generated by the flow of current, performs time domain sampling and recording, divides the continuous signal into fixed time periods, collects the frequency amplitude of each acoustic wave signal, analyzes the frequency and amplitude sequence to extract key acoustic wave features, and generates acoustic wave feature data; The user identification module integrates the acoustic wave feature data and the synchronous load data, analyzes the frequency and amplitude change relationship between the acoustic wave data and the load data, calculates the characteristic difference matching degree, identifies the load correlation of the acoustic wave feature, classifies the power consumption pattern of the power users, extracts the power consumption behavior characteristics of each type of user, and generates the power household identification result.

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