An intelligent identification high protection electric energy meter

Abstract

The present invention belongs to the technical field of smart electric meters, and specifically relates to an intelligent identification high-protection electric energy meter; the invention comprises an electric energy meter body and a server communicating with the electric energy meter body, wherein a data acquisition module and an Internet of Things communication module are arranged in the electric energy meter body, and the data acquisition module establishes communication with the server through the Internet of Things communication module; the data acquisition module is used to collect real-time monitoring data, and the real-time monitoring data is uploaded to the server through the Internet of Things communication module, and a storage module is arranged in the server, and the storage module comprises a program storage unit and a data storage unit, and an abnormality identification method is stored in the program storage unit, and the abnormality identification method determines whether the electric energy meter has an abnormality based on the real-time monitoring data and sends abnormal alarm information to the client, and the data storage unit is used to store the real-time monitoring data and the abnormal alarm information acquired by the data acquisition module; the present invention can realize the occurrence of abnormal state in the working process of the electric energy meter by providing an abnormality identification method.

Description

Technical Field

[0001] The invention belongs to the technical field of smart electric meters, and in particular relates to an electric energy meter capable of identifying high-protection electric energy meters. Background Art

[0002] With the continuous development of smart grids, the application of smart energy meters is becoming more and more extensive. The main functions of smart energy meters include measuring energy and communication. Among them, the accuracy of measurement is related to the vital interests of power companies and users, and the communication function is the basis for establishing an automatic meter reading system. Therefore, in order to increase users' dependence on power companies and for the healthy development of the power industry, it is necessary to reduce the failures of smart energy meters during operation.

[0003] At present, manual methods are mostly used to identify faults of smart energy meters, so as to determine the response plan according to the specific fault type, thereby reducing the losses caused to users and power supply companies. Specifically, when the user of the smart energy meter finds that the smart energy meter may have a fault, he informs the operation and maintenance personnel, and then the operation and maintenance personnel disassemble the smart energy meter that may have a fault, and tests the disassembled smart energy meter. After that, the fault type of the smart energy meter is determined based on work experience. However, since the operation and maintenance personnel need to rush to the site to disassemble the smart energy meter, and it takes a long time to manually identify the fault of the smart energy meter, the efficiency of the fault identification of the smart energy meter will be reduced, and since the operation and maintenance personnel use their work experience to identify and judge, the accuracy of the identification will be reduced due to the influence of human factors and work experience. In addition, when disassembling the smart energy meter for fault identification, once the operation and maintenance personnel test and judge through incorrect testing methods, the next step of testing and judgment will not be able to continue, so that the fault of the smart energy meter cannot be effectively identified. Summary of the invention

[0004] In order to at least overcome the above-mentioned deficiencies in the prior art, an object of the present invention is to provide an intelligent identification high-protection electric energy meter.

[0005] The technical solution adopted in the embodiment of the present application is as follows:

[0006] In the first aspect, an intelligent identification high-protection electric energy meter includes an electric energy meter body and a server communicating with the electric energy meter body, wherein the electric energy meter body is configured with a data acquisition module and an Internet of Things communication module, and the data acquisition module establishes communication with the server through the Internet of Things communication module; the data acquisition module is used to collect real-time monitoring data, and the real-time monitoring data is uploaded to the server through the Internet of Things communication module, and the server is configured with a storage module, and the storage module includes a program storage unit and a data storage unit, and an abnormality identification method is stored in the program storage unit, and the abnormality identification method determines whether the electric energy meter has an abnormality based on the real-time monitoring data and sends abnormal alarm information to the client, and the data storage unit is used to The real-time monitoring data and abnormal alarm information acquired by the storage data acquisition module; the abnormal identification method specifically includes the following steps: acquiring the real-time monitoring data of the electric energy meter; judging whether the real-time monitoring data of the electric energy meter is abnormal; when the real-time monitoring data of the electric energy meter is abnormal, sending an alarm signal to the client through the alarm unit and issuing an alarm prompt in real time; judging whether the real-time monitoring data of the electric energy meter is abnormal includes: extracting abnormal state activity features in the real-time monitoring data for characterizing the abnormal state of the electric energy meter; comparing the abnormal state activity features with the abnormal activity feature database to determine whether there is an abnormal state, wherein the abnormal activity feature database is used to characterize the electric energy meter situation map corresponding to the abnormal state activity.

[0007] In a first possible implementation manner of the first aspect, the abnormal state activity feature includes a behavior feature and a vector feature corresponding to the behavior feature; the abnormal activity feature database is configured with a behavior feature and a vector feature corresponding to the behavior feature, and the vector feature includes a threshold label; the abnormal state activity feature is compared with the abnormal activity feature library, including comparing the behavior feature of the abnormal state activity feature with the behavior feature in the abnormal activity feature library to obtain a first comparison result, and based on the first comparison result, the vector feature of the abnormal state activity feature is determined to be compared with a threshold of the corresponding vector feature in the abnormal activity feature library to obtain a second comparison result.

[0008] In combination with the first possible implementation manner of the first aspect, in a second possible implementation manner, based on the first comparison result, the vector feature of the abnormal state activity feature is determined to be compared with the threshold of the corresponding vector feature in the abnormal activity feature library to obtain a second comparison result, which specifically includes the following method: when the vector feature of the abnormal state activity feature is not within the threshold range of the corresponding vector feature in the abnormal activity feature library, the second comparison result is a non-abnormal state; when the vector feature of the abnormal state activity feature is within the threshold range of the corresponding vector feature in the abnormal activity feature library, the second comparison result is an abnormal state.

[0009] In combination with the second possible implementation method of the first aspect, in a third possible implementation method, abnormal state activity characteristics for characterizing the abnormal state of the electric energy meter are extracted from the real-time monitoring data, including: processing the real-time monitoring data according to an abnormal state decision network that meets network convergence requirements, and obtaining abnormal state activity characteristics of the abnormal state activities in the real-time monitoring data.

[0010] In combination with the third possible implementation method of the first aspect, in a fourth possible implementation method, constructing an abnormal activity feature database includes the following method: configuring at least two derivative electric energy meters in the same environment as the target electric energy meter, extracting abnormal state activity features of the derivative electric energy meters in the at least two derivative electric energy meters, and establishing an abnormal activity feature database based on the abnormal state activity features of the at least two derivative electric energy meters.

[0011] In combination with the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, an abnormal activity feature database is established based on at least two derived abnormal state activity features of electric energy meters, including the following method: performing feature vector cleaning on the abnormal state activity features of the first derived electric energy meter state activity of the first derived electric energy meter state activity in the derived electric energy meter monitoring data to obtain the abnormal state activity features of the second derived electric energy meter state activity that matches the target electric energy meter configuration environment; or, based on the first derived electric energy meter state activity in the derived electric energy meter monitoring data, constructing a state activity cluster of the second derived electric energy meter state activity that matches the target electric energy meter configuration environment; and constructing the abnormal state activity features of the second derived electric energy meter state activity in the state activity cluster based on an abnormal state decision network that meets network convergence requirements.

[0012] In combination with the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner, the network weight optimization process of the abnormal state decision network includes: determining a reference electric energy meter monitoring data cluster; the reference electric energy meter monitoring data cluster includes at least two first reference electric energy meter monitoring data of a first reference electric energy meter state activity and second reference electric energy meter monitoring data of a second reference electric energy meter state activity; the at least two first reference electric energy meter monitoring data include at least two first reference electric energy meter monitoring data groups, each first reference electric energy meter monitoring data group having electric energy meter monitoring data of a first reference electric energy meter state activity with the same electric energy meter configuration scenario extracted from multiple preset data field templates; determining the abnormal state activity characteristics of the first reference electric energy meter state activity of the first reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster and the second reference electric energy meter monitoring data of the second reference electric energy meter state activity. abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data; based on the initialized abnormal state decision network, feature decision is performed on the first reference electric energy meter monitoring data and the second reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster to obtain the decision abnormal state activity characteristics of the first reference electric energy meter state activity of the first reference electric energy meter monitoring data and the decision abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data; based on the abnormal state activity characteristics and the decision abnormal state activity characteristics of the first reference electric energy meter state activity, the abnormal state activity characteristics and the decision abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight optimization is performed on the initialized abnormal state decision network to obtain the abnormal state decision network that meets the network convergence requirements.

[0013] In combination with the sixth possible implementation manner of the first aspect, in the seventh possible implementation manner, determining the abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data includes: determining the electric energy meter state activity characteristics of each of the second reference electric energy meter monitoring data; based on the electric energy meter state activity characteristics of the second reference electric energy meter monitoring data and the second reference electric energy meter monitoring data, fusing and constructing the abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data.

[0014] In combination with the sixth possible or seventh possible implementation manner of the first aspect, in an eighth possible implementation manner, the reference electric energy meter monitoring data cluster also includes: a third reference electric energy meter monitoring data; the third reference electric energy meter monitoring data is obtained by updating the focus features of the first reference electric energy meter monitoring data, including the following method: determining the abnormal state activity features of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data; performing feature decision on the third reference electric energy meter monitoring data based on the initialized abnormal state decision network to obtain the decision abnormal state activity features of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data; performing network weight optimization on the initialized abnormal state decision network based on the abnormal state activity features and decision abnormal state activity features of the first reference electric energy meter state activity, the abnormal state activity features and decision abnormal state activity features of the second reference electric energy meter state activity, so as to obtain the abnormal state decision network that meets the network convergence requirements.

[0015] In combination with the eighth possible implementation manner of the first aspect, in a ninth possible implementation manner, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements, including the following method: based on the abnormal state activity characteristics and decision abnormal state activity characteristics of the first reference electric energy meter state activity, the abnormal state activity characteristics and decision abnormal state activity characteristics of the second reference electric energy meter state activity, and the abnormal state activity characteristics and decision abnormal state activity characteristics of the third reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

[0016] In the technical solution provided in the embodiment of the present application, an abnormality identification method is provided to detect the occurrence of abnormal conditions during the operation of the electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] The methods, systems and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numbers represent similar mechanisms in the various views of the accompanying drawings.

[0019] Figure 1This is a schematic diagram of the intelligent identification high-protection electricity meter architecture provided in an embodiment of the present application.

[0020] Figure 2 It is a flowchart of a method for identifying abnormal behavior of an electric energy meter according to some embodiments of the present application. DETAILED DESCRIPTION

[0021] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0022] In the following detailed description, numerous specific details are set forth by way of example in order to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that the present application may be practiced without these details. In other cases, well-known methods, procedures, systems, compositions and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

[0023] Flowcharts are used in the present application to illustrate the execution process performed by the system according to the embodiment of the present application. It should be clearly understood that the execution process of the flowchart may not be performed in order. On the contrary, these execution processes may be performed in reverse order or simultaneously. In addition, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0024] Please refer to Figure 1 The embodiment of the present application provides an intelligent identification high protection electric energy meter including an electric energy meter body and a server communicating with the electric energy meter body. In this embodiment, the electric energy meter body is the main functional hardware, and the server is used to communicate with the electric energy meter body to process the data obtained by the electric energy meter to obtain the processed result.

[0025] In this embodiment, a data acquisition module and an IoT communication module are configured in the electric energy meter body, wherein the data acquisition module is used to collect the working data of the electric energy meter. In this embodiment, the working data of the electric energy meter is monitoring data, that is, monitoring processing is achieved through the working data. In this embodiment, the data acquisition module establishes communication with the server through the IoT communication module, and uploads the collected real-time monitoring data to the server through the IoT communication module.

[0026] In this embodiment, a storage module is configured in the server, wherein the storage module includes a program storage unit and a data storage unit. The program storage unit stores an abnormality identification method, which determines whether the electric energy meter has an abnormality based on real-time monitoring data and sends abnormal alarm information to the client. The data storage unit is used to store the real-time monitoring data and abnormal alarm information obtained by the data acquisition module.

[0027] In this embodiment, the storage module is divided into two storage areas, one of which is a program storage unit and the other is a data storage unit. The program storage unit is equivalent to the firmware area, and the read and write permissions of this area are set to read-only mode, and the data stored in it cannot be erased or changed. The data in the data storage unit can be erased or read and written. When the capacity of the data storage area is full, the newly written data will overwrite the earliest historical data.

[0028] In this embodiment, the above modules and units are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. And include at least one software function module that can be stored in the memory in the form of software or firmware or solidified in the operating system (OS) of the electronic device. The processor is used to execute the executable modules stored in the memory, such as the software function modules and computer programs included in the device corresponding to the access control data management method.

[0029] For anomaly identification methods, see Figure 2 , the abnormality identification method specifically includes the following steps:

[0030] Step S200: Obtaining real-time monitoring data of the electric energy meter.

[0031] Step S300: Determine whether the real-time monitoring data of the electric energy meter is abnormal.

[0032] Step S400: When the real-time monitoring data of the electric energy meter is abnormal, an alarm signal is sent to the client through the alarm unit and an alarm prompt is issued in real time.

[0033] For step S200, the acquisition of real-time monitoring data of the electric energy meter is based on the data acquisition module, wherein the real-time monitoring data includes but is not limited to voltage data, frequency data and waveform data. This process is implemented based on the data acquisition module and does not need to be described in detail.

[0034] With respect to step S300, determining whether the real-time monitoring data of the electric energy meter is abnormal includes the following methods:

[0035] Step S310: extracting abnormal state activity features for characterizing abnormal states of electric energy meters from real-time monitoring data.

[0036] In this embodiment, the real-time monitoring data is processed according to an abnormal state decision network that meets the network convergence requirement to obtain abnormal state activity features of abnormal state activities in the real-time monitoring data.

[0037] Step S320: Compare the abnormal state activity characteristics with the abnormal activity characteristic database to obtain a comparison result, wherein the abnormal activity characteristic database is used to characterize the electric energy meter situation map corresponding to the abnormal state activity.

[0038] In this embodiment, an abnormal activity feature database is constructed to compare with the extracted abnormal state activity features, and whether it is abnormal is determined based on the comparison result.

[0039] This step includes two important processes, one is to build an abnormal activity feature database, and the other is feature comparison. For building the abnormal activity feature database, by configuring at least two derivative electric energy meters in the same environment as the target electric energy meter, extracting the abnormal state activity features of the derivative electric energy meters in the at least two derivative electric energy meters, and establishing the abnormal activity feature database based on the abnormal state activity features of the at least two derivative electric energy meters. In this embodiment, the target electric energy meter is the electric energy meter in the present invention, that is, the electric energy meter to be monitored, and the derivative electric energy meter is an electric energy meter set in the same environment as the target electric energy meter, and a sufficient amount of real-time monitoring data is obtained by setting the derivative electric energy meter.

[0040] Since this process is a complex process, its construction specifically includes the following methods:

[0041] Perform feature vector cleaning on the abnormal state activity characteristics of the first derived electric energy meter state activity of the first derived electric energy meter state activity in the derived electric energy meter monitoring data to obtain the abnormal state activity characteristics of the second derived electric energy meter state activity that matches the target electric energy meter configuration environment; or, based on the first derived electric energy meter state activity in the derived electric energy meter monitoring data, construct a state activity cluster of the second derived electric energy meter state activity that matches the target electric energy meter configuration environment.

[0042] The abnormal state activity characteristics of the second derived electric energy meter state activity in the state activity cluster are constructed according to the abnormal state decision network that meets the network convergence requirement.

[0043] In this embodiment, the network weight optimization process for the abnormal state decision network includes:

[0044] Determine a reference electric energy meter monitoring data cluster; the reference electric energy meter monitoring data cluster includes at least two first reference electric energy meter monitoring data of a first reference electric energy meter state activity and second reference electric energy meter monitoring data of a second reference electric energy meter state activity; the at least two first reference electric energy meter monitoring data include at least two first reference electric energy meter monitoring data groups, each first reference electric energy meter monitoring data group includes electric energy meter monitoring data of first reference electric energy meter state activity with the same electric energy meter configuration scenario extracted from multiple preset data field templates.

[0045] An abnormal state activity feature of a first reference electric energy meter state activity of a first reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster and an abnormal state activity feature of a second reference electric energy meter state activity of a second reference electric energy meter monitoring data are determined.

[0046] Based on the initialized abnormal state decision network, feature decisions are performed on the first reference electric energy meter monitoring data and the second reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster to obtain the decision abnormal state activity features of the first reference electric energy meter state activity of the first reference electric energy meter monitoring data and the decision abnormal state activity features of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data.

[0047] Based on the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

[0048] In this embodiment, the network weight optimization process for the normal state decision network includes:

[0049] Determine a reference electric energy meter monitoring data cluster; the reference electric energy meter monitoring data cluster includes at least two first reference electric energy meter monitoring data of a first reference electric energy meter state activity and second reference electric energy meter monitoring data of a second reference electric energy meter state activity; the at least two first reference electric energy meter monitoring data include at least two first reference electric energy meter monitoring data groups, each first reference electric energy meter monitoring data group includes electric energy meter monitoring data of first reference electric energy meter state activity with the same electric energy meter configuration scenario extracted from a plurality of preset data field templates;

[0050] Determine an abnormal state activity feature of a first reference electric energy meter state activity of a first reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster and an abnormal state activity feature of a second reference electric energy meter state activity of a second reference electric energy meter monitoring data;

[0051] Based on the initialized abnormal state decision network, feature decisions are performed on the first reference electric energy meter monitoring data and the second reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster to obtain the decision abnormal state activity features of the first reference electric energy meter state activity of the first reference electric energy meter monitoring data and the decision abnormal state activity features of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data.

[0052] Based on the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

[0053] In this embodiment, the method for determining the abnormal state activity feature of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data includes the following methods:

[0054] An electric energy meter state activity feature of each of the second reference electric energy meter monitoring data is determined.

[0055] Based on the electric energy meter state activity characteristics of the second reference electric energy meter monitoring data and the second reference electric energy meter monitoring data, the abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data are integrated and constructed.

[0056] In this embodiment, the reference electric energy meter monitoring data cluster also includes: third reference electric energy meter monitoring data; the third reference electric energy meter monitoring data is obtained by updating the first reference electric energy meter monitoring data with a focus feature, including the following method:

[0057] Determine the abnormal state activity characteristics of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data; perform feature decision on the third reference electric energy meter monitoring data based on the initialized abnormal state decision network to obtain the decision abnormal state activity characteristics of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data.

[0058] According to the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity, and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

[0059] In this embodiment, the network weight optimization is performed on the initialized abnormal state decision network to obtain the abnormal state decision network that meets the network convergence requirements, including the following methods:

[0060] Based on the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity, the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the third reference electric energy meter state activity, the network weight optimization is performed on the initialized abnormal state decision network to obtain the abnormal state decision network that meets the network convergence requirements.

[0061] Step S330: Determine whether an abnormality occurs based on the comparison result.

[0062] In this embodiment, the abnormal state activity feature includes a behavior feature and a vector feature corresponding to the behavior feature; the abnormal activity feature database is configured with the behavior feature and the vector feature corresponding to the behavior feature, and the vector feature includes a threshold label; the abnormal state activity feature is compared with the abnormal activity feature library, including comparing the behavior feature of the abnormal state activity feature with the behavior feature in the abnormal activity feature library to obtain a first comparison result, and based on the first comparison result, the vector feature of the abnormal state activity feature is determined to be compared with the threshold of the corresponding vector feature in the abnormal activity feature library to obtain a second comparison result.

[0063] In this implementation, the first comparison result determines that the vector feature of the abnormal state activity feature is compared with the threshold of the corresponding vector feature in the abnormal activity feature library to obtain the second comparison result, which specifically includes the following method:

[0064] When the vector feature of the abnormal state activity feature is not within the threshold range of the corresponding vector feature in the abnormal activity feature library, the second comparison result is a non-abnormal state;

[0065] When the vector feature of the abnormal state activity feature is within the threshold range of the corresponding vector feature in the abnormal activity feature library, the second comparison result is an abnormal state.

[0066] Step S400: When the real-time monitoring data of the electric energy meter is abnormal, an alarm signal is sent to the client and an alarm prompt is issued in real time.

[0067] In this embodiment, the alarm signal issuing component is an alarm unit or other communication unit. When the alarm signal is issued, the abnormal data of the electric energy meter and the coding information corresponding to the electric energy meter are issued together. The coding information corresponding to the electric energy meter is obtained when the server communicates with the electric energy meter. Each electric energy meter is provided with corresponding coding information. The coding information includes the relevant configuration of the electric energy meter and the geographical information of the electric energy, which is convenient for the inspection personnel to confirm the maintenance location and maintenance status of the electric energy meter.

[0068] In this embodiment, the above processing method can be configured in a machine-readable storage medium, wherein the machine-readable storage medium can store data and / or instructions. In an exemplary design concept, the machine-readable storage medium can store data obtained from an external terminal. In an exemplary design concept, the machine-readable storage medium can store data and / or instructions for the access control data management system to execute or use to complete the exemplary method described in the present invention. In an exemplary design concept, the machine-readable storage medium may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc. or any combination thereof. An exemplary large-capacity memory may include a disk, an optical disk, a solid-state disk, etc. An exemplary removable memory may include a flash drive, a floppy disk, an optical disk, a memory card, a compressed disk, a tape, etc. An exemplary volatile read-write memory may include a random access memory (RAM). An exemplary RAM may include an active random access memory (DRAM), a double data rate synchronous active random access memory (DDR SDRAM), a passive random access memory (SRAM), a thyristor random access memory (T-RAM), and a zero-capacitance random access memory (Z-RAM), etc. Exemplary read-only memories may include mask read-only memories (MROMs), programmable read-only memories (PROMs), erasable programmable read-only memories (PEROMs), electrically erasable programmable read-only memories (EEPROMs), compact disk read-only memories (CD-ROMs), and digital versatile disk read-only memories, etc. Among them, the memory is used to store programs, and the processor executes the programs after receiving the execution instructions. The communication unit is used to establish a communication connection between the server and the terminal through the network, and to send and receive data through the network. In an exemplary design concept, the machine-readable storage medium can be implemented on a cloud platform. As an example only, the cloud platform can include private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, internal clouds, multi-layer clouds, etc., or any combination thereof.

[0069] The implementation method provided for this embodiment also includes at least one processor executing computer executable instructions stored in a machine-readable storage medium, so that the processor can execute the access control data management method of the above method embodiment, the processor, the machine-readable storage medium and the communication unit are connected according to a bus, and the processor can be used to control the transceiver action of the communication unit. The processor may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0070] It should be understood that, for technical terms that are not explained in the above content, those skilled in the art can unambiguously determine their meanings by deducing from the above disclosed content. For example, for some terms such as thresholds and coefficients, those skilled in the art can deduce and determine them based on the logical relationship between the previous and the next terms. The value ranges of these numerical values ​​can be selected according to actual conditions, such as 0.1 to 1, 1 to 10, and 50 to 100, which are not limited here.

[0071] Those skilled in the art can unambiguously determine some preset, benchmark, predetermined, set and preferred labeled technical features / technical terms based on the above disclosed content, such as thresholds, threshold intervals, threshold ranges, etc. For some unexplained technical feature terms, those skilled in the art are fully capable of reasonably and unambiguously deducing based on the logical relationship of the context, so as to clearly and completely implement the above technical solutions. The prefixes of unexplained technical feature terms, such as "first", "second", "example", "target", etc., can be unambiguously deduced and determined based on the context. The suffixes of unexplained technical feature terms, such as "set", "list", etc., can also be unambiguously deduced and determined based on the context.

[0072] The above contents disclosed in the embodiments of the present application are clear and complete for those skilled in the art. It should be understood that the process of deducing and analyzing the unexplained technical terms based on the above disclosed contents by those skilled in the art is based on the contents recorded in the present application, and therefore the above contents are not a judgment on the creativity of the overall solution.

[0073] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0074] At the same time, the present application uses specific terms to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different parts of this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in at least one embodiment of the present application can be appropriately combined.

[0075] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component" or "system". In addition, various aspects of the present application may be represented as a computer product located in at least one computer-readable medium, the product including a computer-readable program code.

[0076] A computer readable signal medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer readable signal medium may be any computer readable medium other than a computer readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer readable signal medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar medium, or a combination of any of the above mediums.

[0077] The computer program code required for the execution of various aspects of the present application can be written in any combination of one or more programming languages, including object-oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., or similar conventional programming languages, such as "C" programming language, Visual Basic, Fortran2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy or other programming languages. The programming code can be executed entirely on the user's computer, or as an independent software package on the user's computer, or partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0078] In addition, unless expressly stated in the scope of the patent application, the order of the processing elements and sequences described in this application, the use of digital letters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached patent scope is not limited to the disclosed embodiments. On the contrary, the scope of the patent application is intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of the present application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0079] It should also be understood that in order to simplify the description disclosed in this application and thus help understand at least one embodiment of the invention, in the above description of the embodiments of the application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of the application are more than the features mentioned in the claims. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

Claims

1. An intelligent identification high protection electric energy meter, characterized in that: The utility model comprises an electric energy meter body and a server communicating with the electric energy meter body, wherein the electric energy meter body is provided with a data acquisition module and an Internet of Things communication module, and the data acquisition module establishes communication with the server through the Internet of Things communication module; the data acquisition module is used to acquire real-time monitoring data, and the real-time monitoring data is uploaded to the server through the Internet of Things communication module, and the server is provided with a storage module, and the storage module comprises a program storage unit and a data storage unit, and an abnormality identification method is stored in the program storage unit, and the abnormality identification method determines whether the electric energy meter has an abnormality based on the real-time monitoring data and sends abnormality alarm information to the client, and the data storage unit is used to store the real-time monitoring data and abnormality alarm information acquired by the data acquisition module; The abnormality identification method specifically comprises the following steps: Acquiring real-time monitoring data of the electric energy meter; Determining whether the real-time monitoring data of the electric energy meter is abnormal; When the real-time monitoring data of the electric energy meter is abnormal, an alarm signal is sent to the client through the alarm unit and an alarm prompt is issued in real time; Determining whether the real-time monitoring data of the electric energy meter is abnormal includes: Extracting abnormal state activity features for characterizing abnormal state of the electric energy meter from the real-time monitoring data; Comparing the abnormal state activity feature with an abnormal activity feature database to determine whether an abnormal state exists, wherein the abnormal activity feature database is used to characterize the electric energy meter situation map corresponding to the abnormal state activity; The construction of abnormal activity feature database includes the following methods: configuring at least two derivative electric energy meters in the same environment as the target electric energy meter, extracting abnormal state activity features of the derivative electric energy meters in the at least two derivative electric energy meters, and establishing an abnormal activity feature database based on the abnormal state activity features of the at least two derivative electric energy meters; An abnormal activity feature database is established based on at least two derived abnormal state activity features of the electric energy meter, including the following methods: Performing feature vector cleaning on abnormal state activity features of a first derived electric energy meter state activity in the derived electric energy meter monitoring data to obtain abnormal state activity features of a second derived electric energy meter state activity matching the target electric energy meter configuration environment; or, based on the first derived electric energy meter state activity in the derived electric energy meter monitoring data, constructing a state activity cluster of a second derived electric energy meter state activity matching the target electric energy meter configuration environment; Constructing abnormal state activity characteristics of the second derived electric energy meter state activity in the state activity cluster according to an abnormal state decision network that meets network convergence requirements; The network weight optimization process of the abnormal state decision network includes: Determine a reference electric energy meter monitoring data cluster; the reference electric energy meter monitoring data cluster includes at least two first reference electric energy meter monitoring data of a first reference electric energy meter state activity and second reference electric energy meter monitoring data of a second reference electric energy meter state activity; the at least two first reference electric energy meter monitoring data include at least two first reference electric energy meter monitoring data groups, each first reference electric energy meter monitoring data group includes electric energy meter monitoring data of first reference electric energy meter state activity with the same electric energy meter configuration scenario extracted from a plurality of preset data field templates; Determine an abnormal state activity feature of a first reference electric energy meter state activity of a first reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster and an abnormal state activity feature of a second reference electric energy meter state activity of a second reference electric energy meter monitoring data; According to the initialized abnormal state decision network, feature decision is performed on the first reference electric energy meter monitoring data and the second reference electric energy meter monitoring data in the reference electric energy meter monitoring data cluster to obtain the decision abnormal state activity feature of the first reference electric energy meter state activity of the first reference electric energy meter monitoring data and the decision abnormal state activity feature of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data; Based on the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

2. The intelligent identification high protection electric energy meter according to claim 1 is characterized in that: The abnormal state activity feature includes a behavior feature and a vector feature corresponding to the behavior feature; the abnormal activity feature database is configured with a behavior feature and a vector feature corresponding to the behavior feature, and the vector feature includes a threshold label; the abnormal state activity feature is compared with the abnormal activity feature database, including comparing the behavior feature of the abnormal state activity feature with the behavior feature in the abnormal activity feature database to obtain a first comparison result, and based on the first comparison result, the vector feature of the abnormal state activity feature is determined to be compared with a threshold of the corresponding vector feature in the abnormal activity feature database to obtain a second comparison result.

3. The intelligent identification high protection electric energy meter according to claim 2 is characterized in that: Determining based on the first comparison result that the vector feature of the abnormal state activity feature is compared with a threshold of a corresponding vector feature in the abnormal activity feature database to obtain a second comparison result specifically includes the following method: When the vector feature of the abnormal state activity feature is not within the threshold range of the corresponding vector feature in the abnormal activity feature database, the second comparison result is a non-abnormal state; When the vector feature of the abnormal state activity feature is within the threshold range of the corresponding vector feature in the abnormal activity feature database, the second comparison result is an abnormal state.

4. The intelligent identification high protection electric energy meter according to claim 3 is characterized in that: Extracting abnormal state activity features for characterizing the abnormal state of the electric energy meter from the real-time monitoring data includes: The real-time monitoring data is processed according to an abnormal state decision network that meets the network convergence requirement to obtain abnormal state activity features of the abnormal state activities in the real-time monitoring data.

5. The intelligent identification high protection electric energy meter according to claim 4 is characterized in that: The determining of abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data comprises: Determine the electric energy meter state activity characteristics of each of the second reference electric energy meter monitoring data; Based on the electric energy meter state activity characteristics of the second reference electric energy meter monitoring data and the second reference electric energy meter monitoring data, the abnormal state activity characteristics of the second reference electric energy meter state activity of the second reference electric energy meter monitoring data are integrated and constructed.

6. The intelligent identification high protection electric energy meter according to claim 5 is characterized in that: The reference electric energy meter monitoring data cluster also includes: third reference electric energy meter monitoring data; the third reference electric energy meter monitoring data is obtained by updating the first reference electric energy meter monitoring data with a focus feature, including the following method: Determine the abnormal state activity characteristics of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data; perform feature decision on the third reference electric energy meter monitoring data according to the initialized abnormal state decision network to obtain the decision abnormal state activity characteristics of the third reference electric energy meter state activity of the third reference electric energy meter monitoring data; According to the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity, and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, the network weight of the initialized abnormal state decision network is optimized to obtain the abnormal state decision network that meets the network convergence requirements.

7. The intelligent identification high protection electric energy meter according to claim 6 is characterized in that: Optimizing the network weights of the initialized abnormal state decision network to obtain the abnormal state decision network that meets the network convergence requirements, including the following methods: Based on the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the first reference electric energy meter state activity, the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the second reference electric energy meter state activity, and the abnormal state activity characteristics and decision-making abnormal state activity characteristics of the third reference electric energy meter state activity, the network weight optimization is performed on the initialized abnormal state decision network to obtain the abnormal state decision network that meets the network convergence requirements.

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