A method and system for identifying electric energy meters without entering households

Abstract

The present invention relates to the field of string meter identification, and more specifically, the present invention relates to a method and system for identifying a non-household string meter of an electric energy meter. The method comprises: presetting the number of check code bits of a message signal, constructing a reference window of target data, converting power detection data into binary data, and calculating a first abnormality of a target code bit in the target data; obtaining filtered binary data, and calculating a second abnormality of a target code bit in the target data according to the binary data and the filtered binary data; taking the product of the first abnormality and the second abnormality as the total abnormality of the target code bit; calculating the possibility of correction of the target data, traversing different check code bits, and taking the number of check code bits corresponding to the maximum correction possibility as the final check code bit, which is used to receive a message signal to complete string meter identification. Through the technical solution of the present invention, the accuracy of signal output can be improved, thereby improving the speed and accuracy of string meter identification.

Description

Technical Field

[0001] The present invention relates to the field of string meter identification, and more specifically, to a method and system for identifying a string meter without entering a household. Background Art

[0002] In recent years, as the demand for electricity consumption by residents has increased, the phenomenon of meter cross-connection has become more and more common in low-voltage distribution network areas. The phenomenon of meter cross-connection makes it impossible to correspond user data with electricity charges, which not only increases management costs, but also may lead to unfair electricity use among users. Therefore, it is particularly important to check or identify meter cross-connection. In the work of checking meter cross-connection, it is difficult to quickly check and locate users with meter cross-connection due to the huge number of users and meters and the wide scope of the investigation.

[0003] At present, this field has been able to achieve string meter detection without power outage and without entering the house through a non-entering-house string meter instrument. The instrument adopts a message recognition mode. The host sends a message signal at the household side of the electricity meter, and the slave receives the message signal without entering the house and detects the signal strength. The relationship between the household and the meter is determined by comparing and analyzing the signal strength and channel.

[0004] The existing Chinese patent application document with publication number CN112147544A discloses a wireless household-to-household investigation method and device. The method adopts a technical solution of wired transmission and wireless reception, and realizes the verification of the household-to-household line relationship behind the electric energy meter through non-invasive measurement means. By installing an electric power wireless signal transmitting host at the end of the electric energy meter, the electric power signal is transmitted to the user's home, and then the wireless receiving slave is used to perform non-entry signal monitoring, and finally determine whether the user has a household-to-household phenomenon.

[0005] However, during signal transmission, the transmission recognition efficiency will be affected by the length of the check code. If the length of the check code is consistent with the signal length, the recognition is accurate but the recognition efficiency is low. Summary of the invention

[0006] In order to solve the problem of low recognition efficiency, the present invention proposes a method and system for identifying electric energy meters without entering households.

[0007] In a first aspect, the present invention discloses a method for identifying a non-home string meter of an electric energy meter, comprising: presetting the number of check code bits of a message signal, taking any power detection data in the message signal as target data, constructing a reference window of the target data, converting the power detection data into binary data, taking any code bit of the binary data as the target code bit, and calculating a first abnormality degree of the target code bit in the target data; obtaining filtered binary data, calculating a second abnormality degree of the target code bit in the target data according to the binary data and the filtered binary data; taking the product of the first abnormality degree and the second abnormality degree as the total abnormality degree of the target code bit; calculating the correction possibility of the target data, traversing different check code bit numbers, taking the check code bit number corresponding to the maximum correction possibility as the final check code bit number, and using it to receive the message signal to complete the string meter identification; wherein the correction possibility satisfies the relationship: , Indicates the possibility of correction. The serial number representing the code position, Representation code point The total abnormality of Representation code point The output value of Indicates the total number of code positions, code positions This is the target code point.

[0008] By using the abnormality information in the binary representation of power detection data, the contribution of each code position to the recognition accuracy is quantified by comparing the abnormality changes of the original data and the filtered data at the target code position. This not only improves the efficiency of string meter recognition, but also enhances the robustness of the system because it can adapt to different data quality and noise levels and automatically select the number of check code bits that best suits the current conditions. In this way, string meter errors can be identified and corrected more accurately, thereby improving the reliability of the power system and the accuracy of the data.

[0009] Preferably, constructing a reference window for target data includes: constructing a reference window with a preset number of power detection data as a window size, wherein the reference window contains the target data.

[0010] Preferably, the construction of the reference window for the target data further comprises: taking the target data as the center and using the same preset number of continuous power detection data on the left and right sides as the window size to construct the reference window.

[0011] Preferably, the first abnormality degree includes: taking the power detection data except the target data in the reference window as the reference data; calculating the difference between the value of the target code bit in the target data and the value of the target code bit in each reference data, and accumulating all the differences to obtain the difference cumulative value; calculating the ratio of the sequence number of the target code bit in the target data to the total number of code bits, and taking the product of the difference cumulative value and the ratio as the first abnormality degree.

[0012] It can effectively quantify the degree of deviation of target data at key code positions. It reflects the degree of deviation of target data from the normal range at specific code positions, which helps to improve the accuracy of data recognition. It can more effectively screen out possible string table errors, enhance the reliability of data transmission, reduce resource waste and potential safety hazards caused by misidentification, and is of great significance for ensuring the stable operation of the power system and improving the accuracy of data collection.

[0013] Preferably, the first abnormality degree further includes: using the power detection data other than the target data in the reference window as reference data.

[0014] The first abnormality satisfies the relationship: , Indicates the code point in the target data The first abnormality degree, The serial number representing the code position, Indicates the total number of code points, Indicates the total number of reference data, Indicates the serial number of the reference data. Indicates reference data Medium code The value of Indicates the code point in the target data The value, code point This is the target code point.

[0015] Since the data in the signal is continuous, the distance between the reference data and the target data is used as the weight. The smaller the distance, the greater the weight. This takes into account the importance of the code position in the entire data. This quantification helps to identify data points that have large deviations in key locations, thus providing an effective metric for anomaly detection.

[0016] Preferably, obtaining the filtered binary data includes: converting the binary data into decimal data, calculating the anomaly score of the decimal data using isolation forest, calculating the filter window size according to the anomaly score, filtering the decimal data using the filter window, and converting the filtered decimal data into filtered binary data.

[0017] Preferably, the filter window size satisfies the relationship:

[0018] , represents the filter window size, Indicates the magnification. represents the abnormal score of the target data, represents the exponential function, Indicates the maximum allowed window size. Indicates taking the minimum value, Indicates rounding down.

[0019] Data points with higher anomaly scores will result in larger filter window sizes, giving more weight in the filtering process to capture more abnormal features. Conversely, when the degree of anomaly is low, a smaller filter window size can avoid over-processing, thereby reducing interference with normal data.

[0020] Preferably, the second abnormality satisfies the relationship:

[0021] , Indicates the code point in the target data The second abnormality degree, represents the abnormal score of the target data, The serial number representing the code position, Indicates the total number of code points, Indicates the absolute value of the difference between the binary data and the filtered binary data at the target code position, Indicates the maximum value of the code position that produces a numerical change in the filtered binary data. This is the target code point.

[0022] Quantify the degree of abnormality of each code position relative to the overall data distribution, thereby identifying anomalies or outliers in the data, and make adjustments based on the maximum number of code positions that produce numerical changes in the filtered data. This helps to identify and process outliers in the data preprocessing stage and improve the accuracy and reliability of data analysis.

[0023] In a second aspect, the present invention discloses a system for identifying a non-household connected electricity meter, comprising: a processor; and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the system executes the above-mentioned method for identifying a non-household connected electricity meter.

[0024] Beneficial effects of the present invention:

[0025] The present invention can significantly improve the accuracy of string table recognition, while effectively reducing the number of check code bits. In practical applications, reducing the number of redundant check code bits not only reduces the problems of low coding efficiency, high decoding complexity and excessive resource consumption, improves coding efficiency, reduces resource consumption, simplifies the decoding process and reduces complexity.

[0026] By constructing a reference window for target data, accurately calculating the degree of abnormality, and comprehensively analyzing the characteristics of power detection data, the error correction capability of the checksum is enhanced.

[0027] In-depth analysis using the characteristics of power detection data can effectively improve error correction capabilities and ensure data reliability, thereby improving the stability and accuracy of the entire electricity meter identification system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0029] Figure 1 The present invention is a flowchart of a method for identifying a non-household electric energy meter. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0032] The present invention provides a method for identifying an electric energy meter without entering a household. Figure 1 As shown, a method for identifying an electric energy meter that is not connected to a household includes steps S1 to S4, which are described in detail below.

[0033] S1, preset the number of check code bits of the message signal, construct a reference window for the target data, convert the power detection data into binary data, and calculate the first abnormality of the target code bits in the target data.

[0034] It should be noted that the non-entry meter is a device used to detect the correspondence between the electric energy meter and the user in the power system. It can perform on-site power outage detection without entering the user's home by transmitting and receiving near-field communication positioning and identification technology, thereby determining whether the electric energy meter is correctly installed and whether the reading of the electric energy meter correctly corresponds to each user.

[0035] In the field of communication, a message refers to a complete unit of information transmitted between two communicating parties. A message can contain control information and data, and its definition and format depend on the specific communication protocol.

[0036] In the scenario of non-entry-type string meter identification, the message includes not only the basic start code, address code, control code and check code, but also specific message content, that is, in the non-entry-type string meter detection instrument, the message signal generating circuit freely compiles the message according to the requirements of the investigator to prevent the detection of false signals by the slave detection of the detector. These message contents may include specific information for identifying and verifying the corresponding relationship between the electric energy meter and the user.

[0037] The data or event type in the message indicates the type of data or event record to be queried, such as overload, voltage abnormality, etc.

[0038] However, during the transmission and reception of power detection data, abnormal power detection data may exist due to various factors such as noise and environmental interference, which reduces the authenticity and reliability of the entire power detection data. Therefore, it is necessary to identify and correct the abnormal power detection data. Generally, a check code generation algorithm is used to obtain a more accurate check code to increase the accuracy of message signal transmission. Common check code generation algorithms include RS (Reed-Solomon) code, BCH (Bose-Chaudhuri-Hocquenghem) code, QC-LDPC (quasi-cyclic low-density parity check) code and LDPC code (low-density parity check code).

[0039] Preferably, LDPC code is used. LDPC code detects and corrects errors that may occur during transmission by adding check codes. The number of bits of the check code can affect the error correction capability of the data.

[0040] In one embodiment, a message signal is transmitted from a host, a check code bit number of the message signal is preset, any power detection data in the message signal is used as target data, and a reference window for the target data is constructed. Constructing the reference window for the target data includes: constructing a reference window with a preset number of power detection data as the window size, wherein the reference window contains the target data.

[0041] Convert the power detection data into binary data. It should be noted that in binary data, each code bit has only two values, 0 and 1, and the message signal generally has a trend of gradual change, while the abnormal signal usually does not meet the overall signal change trend and will affect the characteristics of the entire signal.

[0042] Taking any code position of binary data as the target code position, calculating the first abnormality degree of the target code position in the target data, the calculation of the first abnormality degree includes: taking the power detection data except the target data in the reference window as the reference data; calculating the difference between the value of the target code position in the target data and the value of the target code position in each reference data, and accumulating all the differences to obtain the difference cumulative value; calculating the ratio of the sequence number of the target code position in the target data to the total number of code positions, and taking the product of the difference cumulative value and the ratio as the first abnormality degree.

[0043] For a plurality of consecutive power detection data, the greater the difference in the values ​​at the same code position, the more likely it is that the power detection data corresponding to the code position is abnormal.

[0044] In one embodiment, the reference window further includes: taking the target data as the center and using the same preset number of continuous power detection data on the left and right sides as the window size to construct the reference window.

[0045] At this time, if the amount of reference data on either side of the reference window does not meet the window size, the reference window is constructed according to the amount of actual reference data.

[0046] Exemplarily, the size of the reference window is set to 21. When the position number of the target data is less than 11, it means that there are not 10 reference data on the left side of the target data. Then the window is constructed according to the reference data that can be obtained, that is, the position number of the target data is 5, then the 4 reference data on the left, the 10 reference data on the right and the target data itself are the reference window. At this time, the window size of the reference window is 15.

[0047] In one embodiment, the first abnormality level further includes: using the power detection data other than the target data in the reference window as reference data.

[0048] The first abnormality satisfies the relationship: , Indicates the code point in the target data The first abnormality degree, The serial number representing the code position, Indicates the total number of code points, Indicates the total number of reference data, Indicates the serial number of the reference data. Indicates reference data Medium code The value of Indicates the code point in the target data The value, code point This is the target code point.

[0049] is the weight of each reference data. Since the distance and weight are inversely proportional, the smaller the distance between the reference data and the target data, the greater the weight of the reference data.

[0050] The first abnormality reflects the similarity between the target code position in the target data and the values ​​of the same code position in multiple consecutive reference data. The larger the first abnormality, the smaller the similarity. The accuracy of the value of the target code position reflected by the first abnormality will be affected by the position of the target code position in the target data, which is reflected in the formula as The first abnormality degree of the high code position reflects the high accuracy of the similarity, and the first abnormality degree of the low code position reflects the low accuracy of the similarity. This is because a slight change in the power detection data will cause a change in the low code position, so it is difficult to distinguish between normal changes and abnormalities in the low code position.

[0051] S2, obtaining the filtered binary data, and calculating a second abnormality degree of a target code position in the target data according to the binary data and the filtered binary data.

[0052] In one embodiment, binary data is converted into decimal data, anomaly scores of the decimal data are calculated using isolation forests, a filter window size is calculated based on the anomaly scores, the decimal data is filtered using the filter window, and the filtered decimal data is converted into filtered binary data.

[0053] It should be explained that the decimal data and the corresponding power detection data have the same value.

[0054] The filter window size satisfies the relationship:

[0055] , represents the filter window size, Indicates the magnification. represents the abnormal score of the target data, represents the exponential function, Indicates the maximum allowed window size. Indicates taking the minimum value, Indicates rounding down.

[0056] For example, the maximum window size is set to 10 and the magnification is set to 4. If the maximum window size is not taken, then The value of must be less than or equal to 9, that is Less than or equal to 8, the maximum value of the exponential function range is 8, The maximum value is 2, so the anomaly score needs to be less than or equal to 0.75.

[0057] It should be noted that the reason for adjusting the filter window using the size of the anomaly score is that if a data point has a high anomaly score, a larger filter window size will be selected to include more neighboring points to smooth or remove the impact of the outlier. In this case, a larger filter window may help reduce the impact of outliers on the overall signal, thereby improving the filtering effect.

[0058] The second abnormality satisfies the relationship:

[0059] , Indicates the code point in the target data The second abnormality degree, represents the abnormal score of the target data, The serial number representing the code position, Indicates the total number of code points, Indicates the absolute value of the difference between the binary data and the filtered binary data at the target code position, Indicates the maximum value of the code position that produces a numerical change in the filtered binary data. This is the target code point.

[0060] For example, the filtered binary data is 011101010011, the original binary data is 011001010111, and the code bits whose values ​​are changed are the 3rd and 9th code bits. Then the maximum value of the sequence number among all the changed code bits is ,Right now .

[0061] The larger it is, the more abnormal the high code bits of the target data are, and the greater the abnormality of the overall data.

[0062] The value of is also 0 or 1. When the value is 0, it means that the value of the binary data and the filtered binary data at the same code position remains unchanged. The impact is small; when the value is 1, it means that the value of the binary data and the filtered binary data at the same code position changes. The overall value is 1, and the second abnormality also increases, because Is a number less than or equal to 1.

[0063] But when When the value is 1, Whether the change has no effect on the overall situation, it is necessary to A second degree of abnormality is determined.

[0064] S3, taking the product of the first abnormality degree and the second abnormality degree as the total abnormality degree of the target code position.

[0065] S4, calculate the possibility of correction of the target data, traverse different check code bit numbers, and use the check code bit number corresponding to the maximum correction possibility as the final check code bit number, which is used to receive the message signal to complete the string table recognition.

[0066] In one embodiment, when the slave receives a message signal, the check code determines the check equation, checks the power detection data obtained after the transmission, and when an error occurs, records all situations that may cause the current error.

[0067] For any case, the correction probability of the target data is calculated, and the correction probability satisfies the relationship:

[0068] , Indicates the possibility of correction. The serial number representing the code position, Representation code point The total abnormality of Representation code point The output value of Indicates the total number of code positions, code positions This is the target code point.

[0069] It should be noted that if the code position The value of is normal. is 0; if the code position The value of is abnormal, is 1.

[0070] use Representation code point The degree of influence in the target data is similar to decimal data. The values ​​of different code positions in binary data have different meanings. For example, the code position value of sequence number 1 is 1, which means 1, while the code position value of sequence number 2 is 1, which means 2, and the code position value of sequence number 3 is 1, which means 4, and so on. The code value is 1, which means Therefore, the change in the value of each code bit has a different degree of influence on the entire power detection data.

[0071] For example, if an error occurs and corresponds to three different situations, then each situation has a total abnormality and output value, that is, three correction possibilities can be calculated, and the maximum correction possibility is selected as the correction degree corresponding to the current preset number of check code bits.

[0072] Traverse different numbers of check code bits, take the number of check code bits corresponding to the maximum correction possibility as the final number of check code bits, take the corrected power detection data as the received message signal, detect the signal strength, and determine the household-meter relationship by comparing and analyzing the signal strength and channel.

[0073] An embodiment of the present invention further discloses a system for identifying a non-household connected meter for an electric energy meter, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for identifying a non-household connected meter for an electric energy meter according to the present invention is implemented.

[0074] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0075] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0076] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0077] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying electric energy meters without entering households, characterized in that: include: The number of check code bits of the message signal is preset, any power detection data in the message signal is used as the target data, a reference window of the target data is constructed, the power detection data is converted into binary data, any code bit of the binary data is used as the target code bit, and the first abnormality of the target code bit in the target data is calculated. The first abnormality includes: The power detection data except the target data in the reference window is used as reference data; The first abnormality satisfies the relationship: , Indicates the code point in the target data The first abnormality degree, Indicates the serial number of the code position, Indicates the total number of code points, Indicates the total number of reference data, Indicates the serial number of the reference data. Indicates reference data Medium code The value of Indicates the code point in the target data The value, code point That is the target code position; Obtain the filtered binary data, and calculate the second abnormality of the target code position in the target data according to the binary data and the filtered binary data. The second abnormality satisfies the relationship: , Indicates the code point in the target data The second abnormality degree, represents the abnormal score of the target data, Indicates the serial number of the code position, Indicates the total number of code points, Indicates the absolute value of the difference between the binary data and the filtered binary data at the target code position, Indicates the maximum value of the code position that produces a numerical change in the filtered binary data. That is the target code position; The product of the first abnormality degree and the second abnormality degree is taken as the total abnormality degree of the target code position; Calculate the possibility of correction of the target data, traverse different check code digits, and use the check code digit corresponding to the maximum correction possibility as the final check code digit for receiving message signals to complete string table recognition; Among them, the correction possibility satisfies the relationship: , Indicates the possibility of correction, Indicates the serial number of the code position, Representation code point The total abnormality of Representation code point The output value of Indicates the total number of code positions, code positions This is the target code point.

2. A method for identifying electric energy meters without entering households according to claim 1, characterized in that: The reference window for constructing target data includes: A reference window is constructed with a preset number of power detection data as the window size, wherein the reference window contains the target data.

3. A method for identifying electric energy meters without entering households according to claim 1, characterized in that: The reference window for constructing target data also includes: Taking the target data as the center, the reference window is constructed by taking the same preset number of continuous power detection data on the left and right sides as the window size.

4. A method for identifying electric energy meters without entering households according to claim 1, characterized in that: The first abnormality degree includes: The power detection data except the target data in the reference window is used as reference data; Calculate the difference between the value of the target code position in the target data and the value of the target code position in each reference data, and accumulate all the differences to obtain a difference accumulation value; The ratio of the sequence number of the target code position in the target data to the total number of code positions is calculated, and the product of the difference accumulation value and the ratio is taken as the first abnormality degree.

5. A method for identifying electric energy meters without entering households according to claim 1, characterized in that: Acquiring the filtered binary data includes: The binary data is converted into decimal data, anomaly scores of the decimal data are calculated using isolation forest, a filter window size is calculated based on the anomaly score, the decimal data is filtered using the filter window, and the filtered decimal data is converted into filtered binary data.

6. A method for identifying electric energy meters without entering households according to claim 5, characterized in that: The filter window size satisfies the relationship: , represents the filter window size, Indicates the magnification. represents the abnormal score of the target data, represents the exponential function, Indicates the maximum allowed window size. Indicates taking the minimum value, Indicates rounding down.

7. A system for identifying electric energy meters without entering households, characterized in that: include: Processor; and A memory storing computer instructions, wherein when the computer instructions are executed by a processor, the system executes a method for identifying a non-household string meter of an electric energy meter according to any one of claims 1 to 6.

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