Electric energy meter automatic identification management method and system based on RFID

Abstract

The invention discloses an RFID-based electric energy meter automatic identification management method and system. The automatic identification management method for the electric energy meter comprises the following steps: writing electric energy meter information into an RFID tag equipped on the electric energy meter through an RFID read-write device, reading the electric energy meter information in the RFID tag of the electric energy meter, and verifying and analyzing the electric energy meter information to obtain verified and analyzed electric energy meter information data; and performing formatting processing on the verified and analyzed electric energy meter information data, performing analysis and identification on the formatted electric energy meter information data to obtain an identification result, and optimizing an electric energy meter management strategy according to the identification result to realize automatic identification management of the electric energy meter. The technical problems that the accuracy and integrity of electric energy meter information data cannot be effectively ensured and the state of the electric energy meter cannot be accurately analyzed according to the electric energy meter information in the existing automatic identification management implementation process of the electric energy meter can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy meter identification, and in particular to an automatic identification management method and system for electric energy meters based on RFID. Background Art

[0002] With the continuous development of smart grids and the advancement of electricity meter technology, the management and maintenance of electricity meters have become more and more complex and important. The traditional management method of electricity meters mainly relies on manual recording and manual input, which has many disadvantages, such as data entry errors, information loss, and low management efficiency. In addition, there are a large number of electricity meters, and the management and maintenance workload is huge. How to achieve efficient management of electricity meter information has become an urgent problem to be solved. At present, RFID (radio frequency identification) technology has been widely used in logistics, warehousing, retail and other fields. The use of RFID technology to automatically identify and manage items has the advantages of high efficiency, accuracy and convenience. Applying RFID technology to electricity meter management can realize the automatic collection and identification of electricity meter information, improve management efficiency and data accuracy. However, relying solely on RFID technology to collect and identify electricity meter information cannot meet the needs of intelligent management.

[0003] Existing electricity meter management systems usually lack intelligent anomaly detection and maintenance mechanisms, and are unable to detect and handle abnormal conditions of electricity meters in a timely manner. If abnormal electricity meters are not handled in a timely manner, it may lead to inaccurate electricity metering and even cause safety hazards. In addition, existing systems often lack efficient analysis and recognition algorithms in data processing, and are unable to fully utilize the collected electricity meter information for intelligent management.

[0004] The above technology has at least the following technical problems: in the process of realizing automatic identification and management of electric energy meters, the accuracy and completeness of electric energy meter information data cannot be effectively guaranteed, and the status of the electric energy meter cannot be accurately analyzed based on the electric energy meter information. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an RFID-based electric energy meter automatic identification management method and system to solve the technical problems that the accuracy and completeness of the electric energy meter information data cannot be effectively guaranteed during the existing automatic identification management of the electric energy meter, and the electric energy meter status cannot be accurately analyzed based on the electric energy meter information.

[0006] To this end, the present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides an automatic identification and management method of an electric energy meter based on RFID, which comprises the steps of:

[0008] S1. Write the energy meter information into the RFID tag equipped with the energy meter through the RFID reader / writer, read the energy meter information in the RFID tag of the energy meter, and verify and parse the energy meter information to obtain the verified and parsed energy meter information data;

[0009] S2. Format the verified and analyzed electric energy meter information data, and then analyze and identify the formatted electric energy meter information data to obtain an identification result, optimize the electric energy meter management strategy according to the identification result, and realize automatic identification and management of the electric energy meter.

[0010] Further, the read electric energy meter information is preprocessed to obtain preprocessed electric energy meter information; then the preprocessed electric energy meter information is verified and analyzed using a multiple verification and analysis algorithm, specifically including:

[0011] The preprocessed electric energy meter information data is formed into a vector T, which contains all the field data of the electric energy meter information, expressed as:

[0012] T=[r1,r2,…,r i ,…,r n ]

[0013] Among them, r i represents the i-th field data of the preprocessed electric energy meter information data, and n is the number of fields;

[0014] The pre-processed electric energy meter information data is segmented and nonlinearly transformed to make the data more suitable for subsequent processing. Each segment of data S j Calculate using the following formula:

[0015]

[0016] Among them, p j and q j are the start and end indexes of the segment, Used to increase the nonlinear characteristics of data;

[0017] After completing the segmentation process, for each segment of data S j Perform multiple checks to ensure the integrity and accuracy of the data; j Perform M and N checks respectively, and get the multiple check formula, which is expressed as:

[0018]

[0019] Among them, C j To verify the results, used to verify the integrity of the data; Indicates the segmented data of the kth check; M is the number of first checks; N is the number of second checks; P is a predefined prime number used for modular operations during the check process; Indicates the segmented data of the lth check; It is the first verification of the segmented data. f(k) is the verification function; It is a second check on the segmented data. g(l) is the verification function; combining the two verification results gives Perform a modulo operation on the combined result to ensure that the verification result is within a reasonable range and enhance the security and reliability of the data;

[0020] The verification result C j With the expected check value C exp For comparison, when C j =C exp , the data integrity verification is passed, and the data is considered complete and accurate; when C j ≠C exp ,Data integrity verification failed, there may be data tampering or transmission errors, further investigation and processing are needed.

[0021] Furthermore, for each segment of data S after verification j Perform iterative parsing to convert it into a recognizable format. The parsing formula is:

[0022]

[0023] Among them, P j (t) is the result of the t-th iteration analysis, which is used to represent the standardized format of each segment of data after the iterative analysis; t is the current iteration number, which represents the current iteration step; j is the index of the segmented data, which represents the j-th segment of data; P j (t-1) is the segmented data result of the previous iteration; b is the parsing base, which controls the amplitude of data parsing; δ is the offset, which is used to adjust the starting point of data parsing; e is the base of natural logarithm, which is used for exponential operation;

[0024] Finally, the iteratively parsed data is obtained, that is, the verified parsed electric energy meter information data P j (T) , T is the total number of iterations.

[0025] Furthermore, the electric energy meter information data P after verification and analysis is j (T) Perform nonlinear formatting. The formula for nonlinear formatting is:

[0026]

[0027] Among them, F j is the electric energy meter information data after nonlinear formatting. α, β, and γ are formatting parameters, which are used to adjust the power, offset, and scaling of the data respectively. θ j is the phase angle of the data.

[0028] Furthermore, the nonlinearly formatted electric energy meter information data F j Feature extraction is performed to obtain a feature vector set, and the feature vector set is standardized to obtain a standardized feature vector set. The standardized feature vector set is nonlinearly transformed to capture complex feature relationships to obtain a nonlinearly transformed feature vector set.

[0029] Furthermore, the feature vector after nonlinear transformation is subjected to multi-layer mapping to further extract high-level features. The mapping formula for each layer is as follows:

[0030]

[0031] in, is the i-th feature vector after mapping at the k-th layer; is the weight coefficient of the i-th feature vector in the k-th layer; is the bias parameter of the i-th feature vector in the k-th layer; is the i-th eigenvector after the k-1th layer mapping; g i It is an auxiliary variable used to capture complex relationships in the nonlinear transformation process; when performing the first layer of mapping, G i represents the i-th eigenvector after nonlinear transformation.

[0032] Furthermore, based on the feature vector set after multi-layer mapping, a comprehensive feature vector set is obtained:

[0033]

[0034] Among them, K represents the total number of mapping layers; H (k) Represents the feature vector set after the k-th layer mapping, composition.

[0035] Furthermore, the recognition result is calculated by the comprehensive feature vector set to determine the state of the electric energy meter. The calculation formula of the recognition result is as follows:

[0036]

[0037] Among them, R is the recognition result, which is used to determine the state of the electric energy meter; is the first weight parameter, which is used to adjust the influence of the feature in the recognition result; is the bias parameter, which is used to adjust the nonlinear transformation effect of the feature; is the second weight parameter, which is used to adjust the influence of the feature in the recognition result; H j represents the jth eigenvector in the comprehensive eigenvector set; H m represents the mth feature vector in the comprehensive feature vector set; L represents the dimension of the comprehensive feature vector set.

[0038] Furthermore, the recognition result R obtained above is used to determine the state of the electric energy meter, specifically including: setting a threshold When the recognition result is greater than or equal to the threshold When the recognition result is less than the threshold, the energy meter is in normal state; When the power meter is in abnormal state.

[0039] In a second aspect, the present invention provides an RFID-based automatic identification and management system for electric energy meters, which is used to implement the above-mentioned automatic identification and management method for electric energy meters, and includes a tag configuration module, an information reading module, a data processing module, a data analysis module, and a database management module;

[0040] The tag configuration module configures an RFID tag for the electric energy meter, and writes the electric energy meter information into the RFID tag through the RFID reader / writer;

[0041] The information reading module reads the electric energy meter information in the electric energy meter RFID tag and sends the electric energy meter information to the data processing module;

[0042] The data processing module verifies and analyzes the electric energy meter information to obtain the verified and analyzed electric energy meter information data, formats the verified and analyzed electric energy meter information data to obtain the formatted electric energy meter information data, and sends the formatted electric energy meter information data to the database management module and the information analysis module;

[0043] The information analysis module analyzes and identifies the formatted electric energy meter information data to obtain an identification result, optimizes the electric energy meter management strategy according to the identification result, realizes automatic identification and management of the electric energy meter, and stores the identification result in the database management module;

[0044] The database management module stores the data generated in the identification and management of the electric energy meter, supports real-time updating and querying of the information of the electric energy meter, and ensures the timeliness and accuracy of the data.

[0045] The present invention has the following beneficial effects:

[0046] 1. Through multiple checks and analysis of the energy meter information, errors and redundant information in the data can be effectively identified and corrected to ensure the accuracy and completeness of the energy meter information.

[0047] 2. By processing feature data through nonlinear transformation and multi-layer mapping, high-level features can be efficiently extracted and utilized, improving data processing efficiency and recognition accuracy.

[0048] 3. The recognition result is obtained by introducing the recognition result calculation formula, and the state of the electric energy meter is determined based on the recognition result, which can further generate accurate maintenance suggestions, optimize the maintenance plan, and ensure the stable operation of the electric energy meter and extend its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of an automatic identification and management method of electric energy meters based on RFID of the present invention;

[0050] Figure 2 This is a structural diagram of an RFID-based automatic identification and management system for electric energy meters according to the present invention. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] Example 1

[0054] Refer to the attached Figure 1 , which shows a flow chart of an RFID-based electric energy meter automatic identification and management method provided by an embodiment of the present invention, the method comprising the following steps:

[0055] S1. Write the energy meter information into the RFID tag equipped with the energy meter through the RFID reading and writing device, read the energy meter information in the RFID tag of the energy meter, and verify and analyze the energy meter information to obtain the verified and analyzed energy meter information data.

[0056] Each electric energy meter is equipped with an RFID tag, which includes a chip and an antenna, and is used to store and transmit electric energy meter information. The electric energy meter information such as the unique identification information, model, specification, manufacturing date, calibration record and initial information entered by the production line when leaving the factory is written into the RFID tag through the RFID reader / writer.

[0057] Use an RFID reader to read the energy meter information in the RFID tag on the energy meter to obtain the energy meter information, and perform preprocessing on the obtained energy meter information, such as removing redundant information and standardizing, to obtain the preprocessed energy meter information. Use multiple verification and analysis algorithms to verify and analyze the preprocessed energy meter information. The specific implementation process is as follows:

[0058] In order to digitize the energy meter information in the RFID tag and provide a basis for subsequent data processing, the preprocessed energy meter information data is formed into a vector T, which contains all the field data of the energy meter information. It can be expressed as:

[0059] T=[r1,r2,…,r i ,…,r n ]

[0060] Among them, r i Represents the i-th field data of the preprocessed electric energy meter information data, and n is the number of fields.

[0061] The pre-processed electric energy meter information data is segmented and nonlinearly transformed to make the data more suitable for subsequent processing. Each segment of data S j Calculate using the following formula:

[0062]

[0063] Among them, p j and q j are the start and end indexes of the segment, Used to increase nonlinear characteristics of data.

[0064] After completing the segmentation process, for each segment of data S j Perform multiple checks to ensure the integrity and accuracy of the data. j Perform M and N checks respectively to obtain the multiple check formula, which is specifically expressed as:

[0065]

[0066] Among them, C j To verify the results, used to verify the integrity of the data; Indicates the segmented data of the kth check; M is the number of first checks; N is the number of second checks; P is a predefined prime number used for modular operations during the check process; Indicates the segmented data of the lth check; It is the first verification of the segmented data. f(k) is a check function, such as an exponential function; It is a second check on the segmented data. g(l) is a verification function, such as a logarithmic function; combining the two verification results yields The combined result is subjected to a modulo operation to ensure that the verification result is within a reasonable range and to enhance the security and reliability of the data.

[0067] The verification result C j With the expected check value C exp For comparison, when C j =C exp , the data integrity verification is passed, and the data is considered complete and accurate; when C j ≠C exp ,Data integrity verification failed, there may be data tampering or transmission errors, further investigation and processing are needed.

[0068] For each segment of data S after verification j Perform iterative parsing to convert it into a recognizable format. The parsing formula is:

[0069]

[0070] Among them, P j (t) is the result of the t-th iteration analysis, which is used to represent the standardized format of each segment of data after the iterative analysis; t is the current iteration number, which represents the current iteration step; j is the index of the segmented data, which represents the j-th segment of data; P j (t-1) is the segmented data result of the previous iteration; b is the parsing base, which controls the amplitude of data parsing; δ is the offset, which is used to adjust the starting point of data parsing; e is the base of natural logarithm, which is used for exponential operation;

[0071] Finally, the iteratively parsed data is obtained, that is, the verified parsed electric energy meter information data P j (T) , T is the total number of iterations.

[0072] S2. Format the verified and analyzed electric energy meter information data, and then analyze and identify the formatted electric energy meter information data to obtain an identification result, optimize the electric energy meter management strategy according to the identification result, and realize automatic identification and management of the electric energy meter.

[0073] After verification and analysis, the electric energy meter information data P j (T) Perform nonlinear formatting to obtain the final electric energy meter information data. The formatting formula is:

[0074]

[0075] Among them, F jis the electric energy meter information data after nonlinear formatting. α, β, and γ are formatting parameters, which are used to adjust the power, offset, and scaling of the data respectively. θ J is the phase angle of the data.

[0076] The nonlinearly formatted electric energy meter information data F j Feature extraction is performed to obtain a feature vector set, and the feature vector set is standardized to obtain a standardized feature vector set. The standardized feature vector set is nonlinearly transformed to capture complex feature relationships to obtain a nonlinearly transformed feature vector set.

[0077] The feature vector set after nonlinear transformation is subjected to multi-layer mapping to further extract high-level features. The mapping formula for each layer is as follows:

[0078]

[0079] in, is the i-th feature vector after mapping at the k-th layer; is the weight coefficient of the i-th feature vector in the k-th layer; is the bias parameter of the i-th feature vector in the k-th layer; is the i-th eigenvector after the k-1th layer mapping; g i It is an auxiliary variable used to capture complex relationships in the nonlinear transformation process. In particular, when performing the first layer of mapping, G i represents the i-th eigenvector after nonlinear transformation.

[0080] Furthermore, based on the feature vector set after multi-layer mapping, a comprehensive feature vector set is obtained:

[0081]

[0082] Among them, K represents the total number of mapping layers; H (k) Represents the feature vector set after the k-th layer mapping, composition.

[0083] Further, the recognition result is calculated by integrating the feature vector set to determine the state of the electric energy meter. The recognition result calculation formula is as follows:

[0084]

[0085] Among them, R is the recognition result, which is used to determine the state of the electric energy meter; is the first weight parameter, which is used to adjust the influence of the feature in the recognition result; is the bias parameter, which is used to adjust the nonlinear transformation effect of the feature; is the second weight parameter, which is used to adjust the influence of the feature in the recognition result; H j represents a component of the comprehensive feature vector set, that is, the jth feature vector in the comprehensive feature vector set; H m represents another component of the comprehensive feature vector set, that is, the mth feature vector in the comprehensive feature vector set; L represents the dimension of the comprehensive feature vector set.

[0086] The identification result R obtained above is used to determine the state of the electric energy meter, specifically including: setting the threshold When the recognition result is greater than or equal to the threshold, the electric energy meter is in normal state; when the recognition result is less than the threshold, the electric energy meter is in abnormal state.

[0087] The management strategy of the electric energy meter is optimized according to the state determination result of the electric energy meter obtained based on the identification result. Specifically, for the electric energy meter with abnormal state, maintenance suggestions are generated, and the management strategy is adjusted according to the maintenance suggestions to improve management efficiency. The generation of maintenance suggestions is based on a comprehensive analysis of the electric energy meter information, which may include: abnormal type identification: determining the specific type of electric energy meter abnormality, such as inaccurate reading, communication failure, voltage abnormality, etc.; cause analysis: analyzing the possible causes of abnormality based on the historical data and current state of the electric energy meter; priority setting: setting the maintenance priority according to the severity of the abnormality to ensure that key issues are handled first. For example, it is recommended to conduct on-site inspection and repair of the abnormal electric energy meter, or adjust the configuration and parameters of the electric energy meter to ensure its normal operation; the maintenance suggestions may include on-site inspection: it is recommended to arrange technicians to conduct on-site inspection of the abnormal electric energy meter to check possible fault points; parameter adjustment: if the abnormality may be caused by a configuration problem, it is recommended to adjust the configuration and parameters of the electric energy meter, such as recalibration, adjustment of communication settings, etc.; spare parts replacement: if a hardware failure is detected, it is recommended to replace the relevant spare parts.

[0088] In particular, the formatted electric energy meter information data and the electric energy meter status and maintenance suggestions obtained based on the identification results are stored to facilitate real-time updating and query of the electric energy meter information, thereby ensuring the timeliness and accuracy of the data.

[0089] In summary, an automatic identification and management method for electric energy meters based on RFID has been completed.

[0090] Example 2

[0091] Refer to the attached Figure 2 , which shows a structural diagram of an RFID-based electric energy meter automatic identification and management system provided by an embodiment of the present invention. The system includes the following parts: a tag configuration module, an information reading module, a data processing module, a database management module and a data analysis module.

[0092] The tag configuration module configures an RFID tag for the electric energy meter, and writes the electric energy meter information into the RFID tag through an RFID reader / writer; the electric energy meter information may include the unique identification information, model, specification, manufacturing date, calibration record and initial information entered by the production line when the electric energy meter leaves the factory.

[0093] The information reading module reads the electric energy meter information in the electric energy meter RFID tag and sends the electric energy meter information to the data processing module.

[0094] The data processing module verifies and analyzes the electric energy meter information to obtain the verified and analyzed electric energy meter information data, formats the verified and analyzed electric energy meter information data to obtain the formatted electric energy meter information data, and sends the formatted electric energy meter information data to the database management module and the information analysis module.

[0095] The information analysis module analyzes and identifies the formatted electric energy meter information data to obtain an identification result, optimizes the electric energy meter management strategy according to the identification result, realizes automatic identification and management of the electric energy meter, and stores the identification result in the database management module.

[0096] The database management module stores the data generated in the identification and management of the electric energy meter, supports real-time updating and querying of the information of the electric energy meter, and ensures the timeliness and accuracy of the data.

[0097] The functions involved in each module described in this embodiment have been described in detail in Embodiment 1 and will not be described again here.

[0098] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An automatic identification and management method for electric energy meters based on RFID, characterized in that: Includes steps: S1. Write the energy meter information into the RFID tag equipped with the energy meter through the RFID reader / writer, read the energy meter information in the RFID tag of the energy meter, and verify and parse the energy meter information to obtain the verified and parsed energy meter information data; S2. Format the verified and analyzed electric energy meter information data, and then analyze and identify the formatted electric energy meter information data to obtain an identification result, optimize the electric energy meter management strategy according to the identification result, and realize automatic identification and management of the electric energy meter.

2. The method for automatic identification and management of electric energy meters according to claim 1, characterized in that: Preprocessing the read electric energy meter information to obtain preprocessed electric energy meter information; Then, the preprocessed electric energy meter information is verified and analyzed using a multiple verification and analysis algorithm, including: The preprocessed electric energy meter information data is formed into a vector T, which contains all the field data of the electric energy meter information, expressed as: T＝[r1,r2,…,r i ,…,r n ] Among them, r i represents the i-th field data of the preprocessed electric energy meter information data, and n is the number of fields; The pre-processed electric energy meter information data is segmented and nonlinearly transformed to make the data more suitable for subsequent processing. Each segment of data S j Calculate using the following formula: Among them, p j and q j are the start and end indexes of the segment, Used to increase the nonlinear characteristics of data; After completing the segmentation process, for each segment of data S j Perform multiple checks to ensure the integrity and accuracy of the data; j Perform M and N checks respectively, and get the multiple check formula, which is expressed as: Among them, C j To verify the results, used to verify the integrity of the data; Indicates the segmented data of the kth check; M is the number of first checks; N is the number of second checks; P is a predefined prime number used for modular operations during the check process; Indicates the segmented data of the lth check; It is the first verification of the segmented data. f(k) is the verification function; It is a second check on the segmented data. g(l) is the verification function; combining the two verification results gives Perform a modulo operation on the combined result to ensure that the verification result is within a reasonable range and enhance the security and reliability of the data; The verification result C j With the expected check value C exp For comparison, when C j =C exp , the data integrity verification is passed, and the data is considered complete and accurate; when C j ≠C exp ,Data integrity verification failed, there may be data tampering or transmission errors, further investigation and processing are needed.

3. The method for automatic identification and management of electric energy meters according to claim 1, characterized in that: For each segment of data S after verification j Perform iterative parsing to convert it into a recognizable format. The parsing formula is: Among them, P j (t) is the result of the t-th iteration analysis, which is used to represent the standardized format of each segment of data after the iterative analysis; t is the current iteration number, which represents the current iteration step; j is the index of the segmented data, which represents the j-th segment of data; P j (t-1) is the segmented data result of the previous iteration; b is the parsing base, which controls the amplitude of data parsing; δ is the offset, which is used to adjust the starting point of data parsing; e is the base of natural logarithm, which is used for exponential operation; Finally, the iteratively parsed data is obtained, that is, the verified parsed electric energy meter information data P j (T) , T is the total number of iterations.

4. The method for automatic identification and management of electric energy meters according to claim 1, characterized in that: After verification and analysis, the electric energy meter information data P j (T) Perform nonlinear formatting. The formula for nonlinear formatting is: Among them, F j is the electric energy meter information data after nonlinear formatting. α, β, and γ are formatting parameters, which are used to adjust the power, offset, and scaling of the data respectively. θ j is the phase angle of the data.

5. The method for automatic identification and management of electric energy meters according to claim 4, characterized in that: The nonlinearly formatted electric energy meter information data F j Feature extraction is performed to obtain a feature vector set, and the feature vector set is standardized to obtain a standardized feature vector set. The standardized feature vector set is nonlinearly transformed to capture complex feature relationships to obtain a nonlinearly transformed feature vector set.

6. The method for automatic identification and management of electric energy meters according to claim 5, characterized in that: The feature vector after nonlinear transformation is mapped through multiple layers to further extract high-level features. The mapping formula for each layer is as follows: in, is the i-th feature vector after mapping at the k-th layer; is the weight coefficient of the i-th feature vector in the k-th layer; is the bias parameter of the i-th feature vector in the k-th layer; is the i-th eigenvector after the k-1th layer mapping; g i It is an auxiliary variable used to capture complex relationships in the nonlinear transformation process; when performing the first layer of mapping, G i represents the i-th eigenvector after nonlinear transformation.

7. The method for automatic identification and management of electric energy meters according to claim 6, characterized in that: Based on the feature vector set after multi-layer mapping, the comprehensive feature vector set is obtained: Among them, K represents the total number of mapping layers; H (k) Represents the feature vector set after the k-th layer mapping, composition.

8. The method for automatic identification and management of electric energy meters according to claim 7, characterized in that: The recognition result is calculated by the comprehensive feature vector set to determine the state of the electric energy meter. The calculation formula of the recognition result is as follows: Among them, R is the recognition result, which is used to determine the state of the electric energy meter; is the first weight parameter, which is used to adjust the influence of the feature in the recognition result; is the bias parameter, which is used to adjust the nonlinear transformation effect of the feature; is the second weight parameter, which is used to adjust the influence of the feature in the recognition result; H j represents the jth eigenvector in the comprehensive eigenvector set; H m represents the mth feature vector in the comprehensive feature vector set; L represents the dimension of the comprehensive feature vector set.

9. The method for automatic identification and management of electric energy meters according to claim 8, characterized in that: The recognition result R obtained above is used to judge the state of the electric energy meter, specifically including: setting a threshold θ, when the recognition result is greater than or equal to the threshold θ, the electric energy meter state is normal; when the recognition result is less than the threshold θ, the electric energy meter state is abnormal.

10. An automatic identification and management system for electric energy meters based on RFID, used to implement the automatic identification and management method for electric energy meters according to any one of claims 1 to 9, characterized in that: It includes a label configuration module, an information reading module, a data processing module, a data analysis module and a database management module; The tag configuration module configures an RFID tag for the electric energy meter, and writes the electric energy meter information into the RFID tag through the RFID reader / writer; The information reading module reads the electric energy meter information in the electric energy meter RFID tag and sends the electric energy meter information to the data processing module; The data processing module verifies and analyzes the electric energy meter information to obtain the verified and analyzed electric energy meter information data, formats the verified and analyzed electric energy meter information data to obtain the formatted electric energy meter information data, and sends the formatted electric energy meter information data to the database management module and the information analysis module; The information analysis module analyzes and identifies the formatted electric energy meter information data to obtain an identification result, optimizes the electric energy meter management strategy according to the identification result, realizes automatic identification and management of the electric energy meter, and stores the identification result in the database management module; The database management module stores the data generated in the identification and management of the electric energy meter, supports real-time updating and querying of the information of the electric energy meter, and ensures the timeliness and accuracy of the data.

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