A smart distribution box with transformer area identification function

Abstract

This invention relates to the field of electricity meter technology, specifically to a smart distribution box with transformer area identification function. It includes a distribution box body, within which a smart meter is installed. The smart meter is connected to a concentrator, which is connected to a management terminal. The smart meter is equipped with a data acquisition unit for collecting electricity consumption data. The management terminal issues transformer area identification commands, and the concentrator identifies multiple smart meters based on these commands, determining the target smart meter. This invention achieves transformer area identification by configuring a smart meter within the distribution box and using a transformer area identification method configured within the smart meter. Furthermore, by constructing clock calibration and interference signal filtering, it reduces external interference during the identification process, improving the accuracy of transformer area identification.

Description

Technical Field

[0001] This invention relates to the field of electricity meter technology, and more specifically, to an intelligent distribution box with transformer area identification function. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, the stable and reliable operation of the power system has become one of the most important tasks of the power system.

[0003] In electricity information collection systems, a transformer substation (or "transformer area") refers to the power supply range or region of a specific transformer. It is a crucial division for electricity management departments to manage low-voltage users in a refined manner. Currently, most power companies in my country adopt a transformer substation-based management approach for low-voltage electricity users, using the substation as the basic unit for marketing management. This allows for better monitoring of electricity consumption and prevention of electricity theft. As a core device in electricity management, the accurate identification of the transformer substation affiliation of smart meters is of paramount importance.

[0004] In traditional technology, transformer substation identification mainly involves on-site operation by staff. First, a main unit is installed at the output side of the distribution transformer, and then a terminal is installed at the user's end. The main unit sends a test signal to the terminal, and the user's ability to receive the test signal determines whether the user belongs to the same distribution transformer as the main unit.

[0005] However, external signal interference can cause inaccurate identification of transformer substations, so it is necessary to improve the accuracy of transformer substation identification. Summary of the Invention

[0006] In order to at least overcome the above-mentioned shortcomings in the prior art, the present invention aims to provide an intelligent distribution box with transformer area identification function.

[0007] In a first aspect, the present invention provides a smart distribution box with transformer area identification function, comprising a distribution box body, a smart meter installed inside the distribution box body, the smart meter connected to a concentrator, the concentrator connected to a management terminal, the smart meter being configured with a data acquisition unit for collecting electricity consumption data; the management terminal for issuing transformer area identification commands, the concentrator performing transformer area identification on multiple smart meters based on the transformer area identification commands, and determining a target smart meter; the concentrator being configured with a storage module, the storage module including a program storage unit and a data storage unit, the program storage unit storing a transformer area identification method, which matches the transformer area of ​​the smart meter by receiving the transformer area identification command to determine whether the smart meter is the target smart meter, the data storage unit storing data; the transformer area identification command being issued by the management terminal to multiple... The system sends a command to smart distribution boxes within the specified range, identifies the target smart meter using a distribution area identification method configured within the concentrator, and obtains the target smart meter. The distribution area identification method includes: receiving the distribution area identification command, which includes features of the target smart meter, specifically electricity consumption data features used to characterize a specific state of the target smart meter; performing clock correction on the smart meter; extracting electricity consumption data features from the smart meter collected by the data acquisition unit, the electricity consumption data features used to characterize a specific state of the smart meter; comparing the electricity consumption data features of the smart meter with those of the target smart meter to obtain a comparison result; and determining whether the smart meter is the target smart meter based on the comparison result.

[0008] In a first possible implementation of the first aspect, clock calibration of the smart meter includes the following method: based on a received substation identification command, obtaining current clock information; comparing the current clock information with preset clock reference information to obtain clock deviation information; and determining the numerical information of clock calibration for each time during the execution period of the command based on a preset number of clock calibrations and the deviation information.

[0009] In conjunction with the first possible implementation of the first aspect, in the second possible implementation, the numerical information of clock correction for each time during the command execution period is determined based on the preset number of clock corrections and the deviation information. Specifically, this includes the following method: processing the determined deviation information and the number of clock corrections to obtain a correction value; the number of clock corrections is determined based on the period duration of the clock interrupt, specifically including: selecting the period duration of a clock interrupt as the period duration of the clock interrupt in the current timing period; and, based on the preset correspondence between the period duration of the clock interrupt and the number of clock corrections, using the number of clock corrections corresponding to the selected period duration of the clock interrupt as the set number of clock corrections.

[0010] In conjunction with the second possible implementation of the first aspect, in the third possible implementation, the clock information of the smart meter is corrected using the correction value when each clock interruption cycle arrives.

[0011] In conjunction with the first possible implementation of the first aspect, in the fourth possible implementation, the current clock information is compared with the preset clock reference information to obtain clock deviation information. Specifically, this includes: comparing the current clock information of the smart meter with the preset clock reference information of the smart meter to obtain a clock reference difference; and comparing the clock reference difference with the duration of the timing period to obtain clock deviation information within the current timing period.

[0012] In the fifth possible implementation of the first aspect, noise processing is further included before the special extraction of the electricity consumption data, including: filtering the electricity consumption data based on a pre-set interference activity feature database, specifically including the following methods: constructing an interference activity feature database; extracting interference state activity features involved in the interference state activities in the electricity consumption data; comparing the interference state activity features with the interference activity feature database to obtain interference information; sending the interference information to a filter, and filtering the electricity consumption data based on the filter.

[0013] In conjunction with the fifth possible implementation of the first aspect, in the sixth possible implementation, the determination of the interference state activity characteristics includes the following methods: extracting the electricity consumption data involving interference state activities; processing the electricity consumption data of multiple smart meters according to a decision network that meets the network convergence requirements, and obtaining the interference state activity characteristics of the interference state activities in the electricity consumption data.

[0014] In conjunction with the sixth possible implementation of the first aspect, the seventh possible implementation involves constructing an interference activity feature database using the following methods: constructing interference state activity features of a basic electricity meter and interference state activity features of multiple derived electricity meters corresponding to the basic electricity meter; determining the distribution of interference state attributes based on the interference state activity features of the basic electricity meter and the multiple derived electricity meter interference state activity features; determining the interference state activity features of the interference activity feature database based on the interference state activity features and the interference state attribute distribution; and constructing a basic electricity meter interference activity feature database corresponding to the interference state activity of the basic electricity meter monitoring data based on the interference state activity features of the interference activity feature database.

[0015] In conjunction with the seventh possible implementation of the first aspect, in the eighth possible implementation, based on the interference state activity features of the basic smart meter and the interference state activity features of multiple derivative meters, the distribution of interference state attributes is determined, including the following methods: fusing the interference state activity features of the first derivative meter state activities, which are respectively involved in historical monitoring data of at least two derivative meters, with the interference state activity features of the interfering meter state activities to obtain the interference state attribute distributions involved in the interference state activity features of at least two first derivative meter state activities; performing feature association referencing on the interference state activity features of the interfering meter state activities and the interference state activity features of the first derivative meter state activities to obtain the temporary interference state attribute distributions involved in the interference state activity features of at least two first derivative meter state activities; determining the interference state attribute distribution corresponding to each interference state activity feature of the first derivative meter state activities based on the temporary interference state attribute distributions corresponding to the interference state activity features of each of the at least two first derivative meter state activities; wherein, the interference state activity features based on the interference state activity features of at least two first derivative meter state activities... The distribution of temporary interference state attributes corresponding to the interference state activity features of each first derived meter state activity in the activity features is determined by: determining a first type of interference state activity feature from the interference state activity features of each first derived meter state activity that expresses the clustered features of the first derived meter state activity with the interference activity feature database; performing feature vector cleaning on the temporary interference state attribute distribution corresponding to the first type of interference state activity feature in the interference state activity features of the first derived meter state activity to obtain a first interference state attribute distribution; determining the temporary interference state attribute distribution corresponding to the second type of interference state activity feature in the interference state activity features of the first derived meter state activity as a second interference state attribute distribution; the second type of interference state activity feature is the interference state activity feature in the interference state activity features of the first derived meter state activity other than the first type of interference state activity feature; and obtaining the interference state attribute distribution of each first derived meter state activity interference state activity interference state activity feature based on the first interference state attribute distribution and the second interference state attribute distribution.

[0016] In conjunction with the eighth possible implementation of the first aspect, in the ninth possible implementation, determining the interference state activity features of the interference activity feature database based on the interference state activity features and the interference state attribute distribution includes: determining the interference state activity features of the interference activity feature database by considering the interference state activity features of the second derived meter state activities matching the target meter configuration environment, and the interference state attribute distributions involved in the interference state activity features of at least two first derived meter state activities; including the following method: constructing a global feature association map of the interference state activity features of at least two second derived meter state activities based on the interference state activity features of at least two second derived meter state activities; constructing the interference state activity features of the interference activity feature database based on the interference state activity features of at least two second derived meter state activities, the global feature association map, and the interference state attribute distributions involved in the interference state activity features of at least two first derived meter state activities. The process of constructing the interference activity features of the interference activity feature database based on the interference activity features of at least two second derived meter state activities, the global feature association map, and the interference state attribute distributions of the interference activity features of at least two first derived meter state activities includes: determining feature connection information for each interference activity feature of the interference activity of the second derived meter state activity based on the interference activity features of each of the at least two second derived meter state activities and the global feature association map; aggregating the feature connection information of the interference activity features of the at least two second derived meter state activities based on the interference state attribute distributions of the interference activity features of the at least two first derived meter state activities; and constructing the interference activity features of the interference activity feature database based on the aggregated feature connection information and the global feature association map.

[0017] According to any of the above-mentioned feasible methods, smart meters are installed in the distribution box, and the identification of smart meter distribution areas is achieved based on the distribution area identification method configured in the smart meters. Furthermore, by constructing clock calibration and interference signal filtering processing, external interference to the identification process is reduced, thereby improving the accuracy of distribution area identification. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be extracted from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of an intelligent distribution box with transformer area identification function provided in an embodiment of the present invention;

[0020] Figure 2 The flowchart of the method for implementing the above-mentioned intelligent distribution box with transformer area identification function is provided in the embodiment of the present invention. Detailed Implementation

[0021] The following description is provided to enable those skilled in the art to practice and follow the invention, and is given in a specific application scenario and with specific requirements. It will be apparent to those skilled in the art that various modifications can be made to the disclosed embodiments, and the general principles defined in this invention can be applied to other embodiments and application scenarios without departing from the principles and scope of the invention. Therefore, the invention is not limited to the described embodiments, but should be given the broadest scope consistent with the claims.

[0022] The terminology used in this invention is for describing specific exemplary embodiments only and does not limit the scope of the invention. The singular forms “a,” “an,” and “the” used in this invention can also include the plural forms, unless the context clearly indicates otherwise. It should also be understood that, as in this specification, the terms “comprising” and “including” indicate only the presence of the stated features, integrals, steps, operations, components, and / or parts, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, components, parts, and / or combinations thereof.

[0023] Based on the following description of the accompanying drawings, which form part of this specification, these and other features, characteristics, functions and operating methods of the related structural elements, as well as the component combinations and manufacturing economics, will become more apparent. However, it should be understood that the drawings are for illustrative and descriptive purposes only and are not intended to limit the scope of the invention. It should also be understood that the drawings are not drawn to scale.

[0024] Flowcharts are used in this invention to illustrate the operations performed by a system based on some embodiments of the invention. It should be understood that the operations in the flowchart may not be performed sequentially. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, one or more other operations may be added to the flowchart, or one or more operations may be deleted from it.

[0025] The present invention will now be described in detail with reference to the accompanying drawings. The specific operation methods in the method embodiments can also be applied to the device embodiments or system embodiments.

[0026] Figure 1 This is an embodiment of the present invention, which provides an intelligent distribution box with a transformer area identification function. The distribution box is described in detail below.

[0027] In this embodiment, a smart distribution box with transformer area identification function includes a distribution box body, a smart meter is installed inside the distribution box body, the smart meter is connected to a concentrator, the concentrator is connected to a management terminal, and the smart meter is configured with a data acquisition unit for collecting electricity consumption data.

[0028] In this embodiment, the management terminal is used to issue a distribution area identification command. The concentrator identifies multiple smart meters based on the distribution area identification command and determines the target smart meter. The concentrator is equipped with a storage module, which includes a program storage unit and a data storage unit. The program storage unit stores the distribution area identification method and matches the distribution area of ​​the smart meters by receiving the distribution area identification command to determine whether the smart meter is the target smart meter. The data storage unit stores the data.

[0029] In this embodiment, the storage module is divided into two storage areas: a program storage unit and a data storage unit. The program storage unit is equivalent to the firmware area, and its read / write permissions are set to read-only mode, meaning the data stored therein cannot be erased or modified. The data storage unit, on the other hand, allows for erasure and reading / writing. When the data storage area is full, newly written data will overwrite the oldest historical data.

[0030] In this embodiment, the transformer area identification command is sent from the management terminal to multiple smart distribution boxes within a range. The transformer area identification method configured in the concentrator is used to identify the transformer area and obtain the target smart meter.

[0031] For information on the method of identifying transformer substations, please refer to [link / reference]. Figure 2 Specifically, it includes the following methods:

[0032] Step S200. Receive the station area identification command.

[0033] In this embodiment, the transformer substation identification command includes the characteristics of the target smart meter. These characteristics are electricity consumption data features, which characterize the specific state of the target smart meter. Configuring these characteristics in the transformer substation identification command is used for subsequent meter matching processing during target meter identification.

[0034] Step S300. Perform clock calibration on the smart meter.

[0035] In this embodiment, the purpose of clock calibration is to ensure that the time reference of the smart meter to be identified is aligned with a high degree of accuracy.

[0036] Step S400. Based on the electricity consumption data of the smart meter collected by the data acquisition unit, the electricity consumption data is filtered to obtain filtered electricity consumption data.

[0037] In this embodiment, because the identification of power distribution areas may be inaccurate due to external interference, the collected power consumption data needs to be filtered to achieve accurate identification of power distribution areas.

[0038] Step S500. Extract features from the filtered electricity consumption data to obtain the filtered electricity consumption data features.

[0039] The electricity consumption data features of the smart meter are used to characterize the specific state of the smart meter. In this embodiment, feature extraction is used for subsequent transformer area identification and comparison.

[0040] Step S600. Compare the filtered electricity consumption data features with the electricity consumption data features of the target smart meter to obtain the comparison result.

[0041] After the subsidy, S700 determines whether the smart meter is the target smart meter based on the comparison results.

[0042] For step S300, clock calibration mainly includes the following methods:

[0043] Step S310. Based on the received station identification command, obtain the current clock information.

[0044] Step S320. Compare the current clock information with the preset clock reference information to obtain the clock deviation information.

[0045] Step S330. Based on the preset number of clock corrections and deviation information, determine the numerical information of each clock correction to be performed during the execution period of this command.

[0046] Step S340. When each clock interruption cycle arrives, the clock information of the smart meter is corrected using the correction value.

[0047] The following specific methods are also included for step S330:

[0048] Step S331. Select a clock interrupt period as the clock interrupt period within the current timing cycle.

[0049] Step S332. Based on the preset correspondence between the clock interrupt cycle duration and the number of clock corrections, the number of clock corrections corresponding to the selected clock interrupt cycle duration is taken as the set number of clock corrections.

[0050] Step S300 in this embodiment obtains the current meter's clock information when a command arrives, compares the current meter's clock information with preset system clock reference information to obtain the system clock deviation information within the current timing period, and determines the numerical information for each clock correction based on the preset number of clock corrections and the deviation information. When each clock interruption cycle arrives, the system clock information is corrected using the numerical information. This ensures that the determined numerical information for each correction of the smart meter's clock information is relatively small, and the meter clock correction is achieved through fine-tuning. This avoids the problem in existing technologies where large correction values ​​cause sudden jumps in the system clock, leading to abnormal operation of the meter's devices, thus improving the system's reliability and stability.

[0051] For step S400, the following process is included:

[0052] Step S410. Construct a database of interference activity characteristics.

[0053] Step S411. Construct the interference state activity characteristics of the basic electricity meter and the interference state activity characteristics of multiple derivative electricity meters corresponding to the basic electricity meter. In this embodiment, the derivative electricity meters are multiple electricity meters with the same configuration and installation environment as the basic electricity meter. The settings of the derivative electricity meters are used to obtain more electricity consumption data and more interference state activity characteristics.

[0054] Step S412. Based on the basic meter interference state activity characteristics and the interference state activity characteristics of multiple derived meters, determine the distribution of interference state attributes.

[0055] This embodiment includes the following methods:

[0056] By fusing the interference state activity features of the first derived meter state activities based on the interference state activity features of the interference meter state activities involving at least two derived meter historical monitoring data, the interference state attribute distributions involving the interference state activity features of the at least two first derived meter state activities are obtained.

[0057] By linking and referencing the interference state activity characteristics of the interfering meter state activity and the interference state activity characteristics of the first derivative meter state activity, the distribution of temporary interference state attributes involved in the interference state activity characteristics of at least two first derivative meter state activities is obtained.

[0058] Based on the distribution of temporary interference state attributes corresponding to the interference state activity features of each of the at least two first derived meter state activities, the distribution of interference state attributes corresponding to the interference state activity features of each first derived meter state activity is determined.

[0059] Specifically, the method for determining the interference state attribute distribution corresponding to each interference state activity of a first derived meter state activity based on the temporary interference state attribute distribution corresponding to each interference state activity characteristic of at least two first derived meter state activities includes the following specific methods:

[0060] From the disturbance state activity features of each first derived meter state activity, determine the first type of disturbance state activity feature that corresponds to the clustered features in the disturbance activity feature database of the first derived meter state activity. Perform feature vector cleaning on the temporary disturbance state attribute distribution corresponding to the first type of disturbance state activity feature in the disturbance state activity features of the first derived meter state activities to obtain the first disturbance state attribute distribution.

[0061] The temporary interference state attribute distribution corresponding to the second type of interference state activity feature in the interference state activity features of the first derived meter state activity is determined as the second interference state attribute distribution. The second type of interference state activity feature refers to the interference state activity features of the first derived meter state activity other than the first type of interference state activity feature.

[0062] Based on the first interference state attribute distribution and the second interference state attribute distribution, the interference state attribute distribution of the interference state activity characteristics of each first derived meter state activity is obtained.

[0063] Step S413. Determine the interference state activity features in the interference activity feature database based on the interference state activity features and the interference state attribute distribution.

[0064] The specific methods include the following: determining the interference state activity characteristics of the interference activity characteristic database by considering the interference state activity characteristics of the second derived meter state activity matching the target meter configuration environment, and the interference state attribute distributions involved in the interference state activity characteristics of at least two first derived meter state activities, including the following methods:

[0065] Based on the interference state activity features of at least two second derived meter state activities, a global feature association map of the interference state activity features of at least two second derived meter state activities is constructed.

[0066] Based on the interference state activity features of at least two second derived meter state activities, the global feature association map, and the interference state attribute distributions involved in the interference state activity features of at least two first derived meter state activities, the interference state activity features of the interference activity database are constructed.

[0067] The method for constructing the interference activity features database based on the interference activity features of at least two second derived meter state activities, the global feature association map, and the interference attribute distributions involved in the interference activity features of at least two first derived meter state activities includes the following specific methods:

[0068] Based on the interference state activity features of each second derived meter state activity in at least two second derived meter state activities, and the global feature association map, the feature connection information of the interference state activity features of each second derived meter state activity is determined.

[0069] Based on the distribution of interference state attributes involved in the interference state activity features of at least two first derived meter state activities, the feature connection information involved in the interference state activity features of at least two second derived meter state activities is aggregated.

[0070] Based on the aggregated feature connection information and the global feature association map, the interference state activity features of the interference activity feature database are constructed.

[0071] Step S414. Construct a basic meter interference activity feature database based on the interference state activity features of the interference activity feature database and the interference state activity corresponding to the basic meter monitoring data.

[0072] Step S420. Extract the interference state activity features involved in the power consumption data.

[0073] Step S430. Compare the interference state activity characteristics with the interference activity characteristic database to obtain interference information.

[0074] In this embodiment, the interference information includes interference features that cause the interference state. The interference features are mainly power consumption information that generates interference, including behavioral features and vector features. The behavioral features are used to characterize the specific data type of power consumption, and the vector features are used to characterize the specific numerical value of the data type of power consumption.

[0075] In this embodiment, the extraction of interference state activity features includes: extracting basic electricity consumption data involving interference state activities, and processing the basic electricity meter consumption data according to the interference state decision network that meets the network convergence requirements to obtain interference state activity features of interference state activities in the basic electricity meter consumption data.

[0076] Step S440. Send the interference information to the filter, and perform filtering processing on the power consumption data based on the filter.

[0077] In this embodiment, a data processing unit is configured within the filter. This unit determines the characteristics of the interfering power consumption data and its specific values ​​based on vector features. Furthermore, a filtering model is configured within the filter, which is a model of each interfering factor and its processing method. This model is constructed by collecting a large amount of data from multiple filters and passing it through a decision tree convergence network. The interfering factors are also interfering power consumption data features; that is, the filtering model is configured with a correspondence between interfering power consumption data features and filter execution behavior features. These interfering power consumption data features include interfering behavior features and interfering vector features. The correspondence between the interfering behavior features, interfering vector features, and filter execution behavior features is generated for the filtering model.

[0078] Determining the correspondence involves first acquiring interference behavior characteristics to determine the specific execution behavior of the filter, then determining the specific numerical range of the filter's execution behavior based on the interference vector characteristics, and finally completing the filtering process.

[0079] For complex filtering behaviors, a composite filtering model can be constructed. First, an interference factor influence model is built, representing a complex noise environment composed of multiple interference factors. Then, through an iterative process, the interfering factors are identified within this complex noise environment, and a composite filtering model is constructed based on these factors. Finally, based on this composite filtering model, a correspondence is established between the characteristics of the interference behavior and the characteristics of the interference vector, and specific filtering processing is performed.

[0080] This embodiment uses filtering to remove interference factors, making the identification of transformer substations more accurate.

[0081] Furthermore, embodiments of the present invention also provide a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned platform area identification method is implemented.

[0082] It should be understood that the above description is for illustrative purposes only and is not intended to limit the scope of the invention. Those skilled in the art can make various modifications and variations based on the description of the invention. However, these modifications and variations will not depart from the scope of the invention.

[0083] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of the invention. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to the invention. Such modifications, improvements, and corrections are suggested in this invention and therefore remain within the spirit and scope of the exemplary embodiments of the invention.

[0084] Furthermore, specific terms are used to describe embodiments of the invention. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the invention. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the invention can be appropriately combined.

[0085] Furthermore, those skilled in the art will understand that aspects of the present invention can be described and illustrated according to several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of the present invention can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software can be referred to as "units," "modules," or "systems." Furthermore, aspects disclosed in this invention can take the form of computer program products embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0086] Computer-readable signal media may contain a propagated data signal containing computer program encoding, for example, on baseband or as part of a carrier wave. Such propagated signals can take many forms, including electromagnetic, optical, and any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can be used to communicate, propagate, or transmit a program for use, connected to an instruction execution system, apparatus, or device. The program encoding located on the computer-readable signal medium can be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination thereof.

[0087] The computer program code required for the operation of each part of this invention can be written in any one or more programming languages, including subject-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, active programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the power operation zone computer, or as a standalone software package on the power operation zone computer, or partially on the power operation zone computer and partially on a remote computer, or entirely on a remote computer.

[0088] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this invention are not intended to limit the order of the processes and methods of this invention. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention based on various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that match the substance and scope of the embodiments of this invention. For example, while the system components described above can be implemented using hardware devices, they can also be implemented using software solutions, such as installing the described system on an existing 5G-based meter monitoring data processing system or mobile device.

[0089] Similarly, it should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof.

Claims

1. A smart distribution box with a transformer identification function, characterized in that, The power distribution box body is provided with a smart electric meter, the smart electric meter is connected with a concentrator, the concentrator is connected with a management terminal, the smart electric meter is provided with a data acquisition unit, and the data acquisition unit is used for acquiring power consumption data; the management terminal is used for issuing a district identification command, the concentrator identifies the district of a plurality of smart electric meters based on the district identification command, and determines a target smart electric meter; The concentrator is provided with a storage module, the storage module includes a program storage unit and a data storage unit, the program storage unit stores a district identification method, the district of the smart electric meter is matched by receiving the district identification command, it is judged whether the smart electric meter is a target smart electric meter, and the data storage unit is used for storing data; The district identification command is sent to the smart power distribution box in a plurality of ranges based on the management terminal, and the district identification method configured in the concentrator is used for district identification to obtain the target smart electric meter; The district identification method comprises: Receiving the district identification command, the district identification command includes the characteristics of the target smart electric meter, the characteristics of the target smart electric meter are power consumption data characteristics, and the power consumption data characteristics of the target smart electric meter are used to represent the specific state of the target smart electric meter; Clock correction is performed on the smart electric meter; Based on the power consumption data of the smart electric meter collected by the data acquisition unit, the power consumption data characteristics of the power consumption data of the smart electric meter are extracted, and the power consumption data characteristics of the smart electric meter are used to represent the specific state of the smart electric meter; The power consumption data characteristics of the smart electric meter are compared with the power consumption data characteristics of the target smart electric meter to obtain a comparison result; Based on the comparison result, it is judged whether the smart electric meter is the target smart electric meter; Before the power consumption data is extracted, noise processing is also included, which comprises: filtering the power consumption data based on a pre-set interference activity characteristic database, which comprises the following methods: Constructing an interference activity characteristic database, extracting interference state activity characteristics related to interference state activities in the power consumption data, and comparing the interference state activity characteristics with the interference activity characteristic database to obtain interference information; The interference information is sent to a filter, and the filter is used to filter the power consumption data; The determination of the interference state activity characteristics comprises the following methods: Extracting the power consumption data related to the interference state activities; According to a decision network meeting the network convergence requirement, the power consumption data of a plurality of smart electric meters is processed to obtain the interference state activity characteristics of the interference state activities in the power consumption data; The construction of the interference activity characteristic database comprises the following methods: Constructing a basic electric meter interference state activity feature and a plurality of derived electric meter interference state activity features corresponding to the basic electric meter; determining an interference state attribute distribution based on the basic electric meter interference state activity feature and the plurality of derived electric meter interference state activity features; determining an interference state activity feature of an interference activity feature database based on the interference state activity feature and the interference state attribute distribution; and constructing a basic electric meter interference activity feature database corresponding to the interference state activity of the basic electric meter monitoring data based on the interference state activity feature of the interference activity feature database.

2. The intelligent distribution box having a transformer identification function according to claim 1, characterized in that, The clock correction of the smart meter includes the following methods: Based on the received substation identification command and the current clock information; Compare the current clock information with the preset clock reference information to obtain the clock deviation information; According to the preset clock correction times and the deviation information, the numerical information of each clock correction in the command execution period is determined.

3. The intelligent distribution box having a transformer identification function according to claim 2, characterized in that, According to the preset clock correction times and the deviation information, the numerical information of each clock correction in the command execution period is determined, which includes the following methods: The determined deviation information and the clock correction times are processed to obtain the correction value; the clock correction times are determined according to the period length of clock interruption, which includes: Select a clock interruption period length as the clock interruption period length in the current timing cycle; According to the corresponding relationship between the preset clock interruption period length and the clock correction times, the clock correction times corresponding to the selected clock interruption period length are set as the set clock correction times.

4. The intelligent distribution panel with a transformer area identification function according to claim 3, characterized in that, When each clock interruption period comes, the clock information of the smart meter is corrected using the correction value.

5. The intelligent distribution panel with feeder identification function as claimed in claim 2 wherein, Compare the current clock information with the preset clock reference information to obtain the clock deviation information, which includes: Compare the current clock information of the smart meter with the preset clock reference information of the smart meter to obtain the clock reference difference value; Compare the clock reference difference value with the length value of the timing cycle to obtain the clock deviation information in the current timing cycle.

6. The intelligent distribution panel with feeder identification function according to claim 1, characterized in that, Based on the basic electric meter interference state activity feature and the plurality of derived electric meter interference state activity features, the interference state attribute distribution is determined, which includes the following methods: Fuse the interference state activity features of the first derived electric meter state activity involved in the historical monitoring data of at least two derived electric meters with the interference state activity features of the interference electric meter state activity to obtain the interference state attribute distribution involved in the interference state activity features of at least two first derived electric meter state activities; Feature association reference is performed on the interference state activity features of the interference electric meter state activity and the interference state activity features of the first derived electric meter state activity to obtain temporary interference state attribute distributions involved in the interference state activity features of at least two first derived electric meter state activities; According to the temporary interference state attribute distribution corresponding to each of the at least two first derivative electric meter state activity interference state activity features, determine the interference state attribute distribution corresponding to each of the first derivative electric meter state activity interference state activity features; According to the temporary interference state attribute distribution corresponding to each of the at least two first derivative electric meter state activity interference state activity features, determine the interference state attribute distribution corresponding to each of the first derivative electric meter state activity interference state activity features; From each of the first derivative electric meter state activity interference state activity features, determine a first interference state activity feature corresponding to the first derivative electric meter state activity and the centralized aggregation feature of the interference activity feature database; perform feature vector cleaning on the temporary interference state attribute distribution corresponding to the first interference state activity feature in the first derivative electric meter state activity interference state activity feature, to obtain a first interference state attribute distribution; Determine the temporary interference state attribute distribution corresponding to a second interference state activity feature in the first derivative electric meter state activity interference state activity feature as a second interference state attribute distribution; the second interference state activity feature is an interference state activity feature in the first derivative electric meter state activity interference state activity feature other than the first interference state activity feature; According to the first interference state attribute distribution and the second interference state attribute distribution, obtain the interference state attribute distribution of each of the first derivative electric meter state activity interference state activity features.

7. The intelligent distribution panel with feeder identification function as claimed in claim 6 wherein, Based on the interference state activity features and the interference state attribute distributions, determine the interference state activity features of the interference activity feature database, including: determining the interference state activity features of the interference activity feature database according to the interference state activity features of the second derivative electric meter state activity matching the target electric meter configuration environment, and the interference state attribute distributions respectively involved by the at least two first derivative electric meter state activity interference state activity features; including the following methods: According to the at least two second derivative electric meter state activity interference state activity features, construct a global feature correlation graph of the at least two second derivative electric meter state activity interference state activity features; According to the at least two second derivative electric meter state activity interference state activity features, the global feature correlation graph, and the interference state attribute distributions respectively involved by the at least two first derivative electric meter state activity interference state activity features, construct the interference state activity features of the interference activity feature database; According to the at least two second derivative electric meter state activity interference state activity features, the global feature correlation graph, and the interference state attribute distributions respectively involved by the at least two first derivative electric meter state activity interference state activity features, construct the interference state activity features of the interference activity feature database; According to the at least two second derivative electric meter state activity interference state activity features, the global feature correlation graph, and the interference state attribute distributions respectively involved by the at least two first derivative electric meter state activity interference state activity features, construct the interference state activity features of the interference activity feature database; determining feature association information of each of the second derived state activity interference state activity features according to each of the second derived state activity interference state activity features and the global feature association graph; aggregating feature association information respectively involved by the at least two second derived state activity interference state activity features according to interference state attribute distributions respectively involved by the at least two first derived state activity interference state activity features; constructing the interference state activity features of the interference activity feature database according to the aggregated feature association information and the global feature association graph.

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