Electric energy metering fault detection method and device, terminal equipment and storage medium

Abstract

The invention discloses an electric energy metering fault detection method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring electrical data of network equipment in a smart power grid within a preset time period; for each network device, according to the electrical data, an electric energy value, an electric quantity differential value, a power differential value and a line loss change value are calculated; inputting the electric energy value, the electric quantity differential value, the power differential value and the line loss change value into a preset fault detection model to obtain data characteristics of the electric energy value, the electric quantity differential value, the power differential value and the line loss change value; obtaining a preset standard data feature range value; comparing each data feature with a preset standard data feature range value; and determining that the corresponding network equipment breaks down when any data feature in the data features exceeds a corresponding preset standard data feature range value. According to the invention, faults of the intelligent electric meter can be detected, and faults of other network devices in an intelligent power grid can also be detected.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to an electric energy metering fault detection method, device, terminal equipment and storage medium. Background Art

[0002] Smart meters are one of the basic devices for smart grid data collection. They are responsible for collecting, measuring and transmitting raw power data and are the basis for information integration, analysis optimization and information presentation. With the advancement of technology, mass-produced static meters can obtain powerful data processing and storage capabilities at a very low cost, which greatly improves the intelligence level of small user meters. Static meters have also gradually replaced traditional electromechanical meters.

[0003] However, when an electric energy metering failure occurs, only the smart meter fault can be checked, but other network devices in the smart grid cannot be detected for faults. Summary of the invention

[0004] The present invention provides an electric energy metering fault detection method, device, terminal equipment and storage medium, which can not only detect faults of smart meters, but also detect faults of other network equipment in the smart grid.

[0005] An embodiment of the present invention provides an electric energy metering fault detection method, comprising:

[0006] Obtaining electrical data of network equipment in a smart grid within a preset period of time; wherein the network equipment includes: a server, a 4G wired Ethernet, a firewall, a switch, and a smart meter; wherein the electrical data includes: current and voltage;

[0007] For each network device, the electric energy value, the electric quantity differential value, the power differential value and the line loss change value are calculated based on the above electrical data;

[0008] Inputting the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency;

[0009] Obtaining preset standard data feature range values; wherein the above standard data feature range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values, and standard root mean square frequency range values;

[0010] Compare each of the above data features with the above preset standard data feature range value;

[0011] When any of the above data features exceeds the corresponding preset standard data feature range value, the corresponding network device fails.

[0012] Furthermore, the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device are calculated based on the above electrical data, including:

[0013] Calculating the product of the current, the voltage and the preset time period to obtain the electric energy value;

[0014] Calculating the integral of the product of the current and the voltage in the preset time period to obtain the electrical quantity differential value;

[0015] Calculate the product of the current and the voltage at the preset time within the preset time period to obtain a first power value;

[0016] Calculate the product of the current and the voltage corresponding to the moment before the preset moment to obtain a second power value;

[0017] Calculating the difference between the second power value and the first power value to obtain the power differential value;

[0018] Calculating the product of the current and the voltage to obtain a third power value for a preset period of time;

[0019] Calculate the product of the current in the previous period and the voltage in the previous period to obtain a fourth power value in the previous period;

[0020] The difference between the third power value and the fourth power value is calculated to obtain the line loss change value.

[0021] Furthermore, after calculating the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device, it also includes:

[0022] Compare the above electric energy values, electric quantity differential values, power differential values ​​and line loss change values ​​with the historical electric energy values, historical electric quantity differential values, historical power differential values ​​and historical line loss change values ​​recorded in the historical database;

[0023] The different values ​​are saved in the above historical database to generate a new historical database to record the detection data information of each device in the smart grid.

[0024] Furthermore, in the above-mentioned determination of the above-mentioned data features, when any data feature exceeds the corresponding preset standard data feature range value, after the corresponding network device fails, it also includes:

[0025] Generate fault information of the failed network device and send the fault information to the user terminal for early warning.

[0026] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0027] The present invention provides an electric energy metering fault detection device, comprising:

[0028] An electrical data acquisition module, an electrical data calculation module, a data feature extraction module, a standard data feature acquisition module, a data feature comparison module, and a fault determination module;

[0029] The electrical data acquisition module is used to acquire electrical data of network devices in the smart grid within a preset period of time; wherein the network devices include: servers, 4G wired Ethernet, firewalls, switches and smart meters; wherein the electrical data include: current and voltage;

[0030] The electrical data calculation module is used to calculate the electric energy value, the electric quantity differential value, the power differential value and the line loss change value for each network device according to the electrical data;

[0031] The data feature extraction module is used to input the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency;

[0032] The standard data feature acquisition module is used to obtain preset standard data feature range values; wherein the standard data feature range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values ​​and standard root mean square frequency range values;

[0033] The data feature comparison module is used to compare each of the data features with the preset standard data feature range value;

[0034] The fault determination module is used to determine that when any data feature among the data features exceeds the corresponding preset standard data feature range value, the corresponding network device fails.

[0035] Furthermore, the electrical data calculation module includes: a data calculation unit;

[0036] The above-mentioned data calculation unit includes: calculating the product of the above-mentioned current, the above-mentioned voltage and the above-mentioned preset time period to obtain the above-mentioned electric energy value; calculating the integral of the product of the above-mentioned current and the above-mentioned voltage in the above-mentioned preset time period to obtain the above-mentioned electric quantity differential value; calculating the product of the current and the voltage at the preset moment in the above-mentioned preset time period to obtain the first power value; calculating the product of the current and the voltage corresponding to the previous moment of the above-mentioned preset moment to obtain the second power value; calculating the difference between the above-mentioned second power value and the above-mentioned first power value to obtain the above-mentioned power differential value; calculating the product of the above-mentioned current and the above-mentioned voltage to obtain the third power value of the preset time period; calculating the product of the current of the previous time period and the voltage of the previous time period to obtain the fourth power value of the previous time period; calculating the difference between the above-mentioned third power value and the above-mentioned fourth power value to obtain the above-mentioned line loss change value.

[0037] Further, it also includes: a data recording module;

[0038] The above-mentioned data recording module is used to compare the above-mentioned electric energy values, electric quantity differential values, power differential values ​​and the above-mentioned line loss change values ​​with the historical electric energy values, historical electric quantity differential values, historical power differential values ​​and historical line loss change values ​​recorded in the historical database; save the different values ​​in the above-mentioned historical database, and generate a new historical database to record the detection data information of each device in the smart grid.

[0039] Furthermore, it also includes: an early warning module;

[0040] The above-mentioned early warning module is used to generate fault information of the failed network device and send the above-mentioned fault information to the user terminal for early warning.

[0041] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0042] The present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, an electric energy metering fault detection method according to any embodiment of the present invention is implemented.

[0043] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0044] The present invention provides a storage medium, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, an electric energy metering fault detection method according to any embodiment of the present invention is implemented.

[0045] The embodiments of the present invention have the following beneficial effects:

[0046] The present invention provides an electric energy metering fault detection method, device, terminal device and storage medium. The method first obtains electrical data of network devices in a smart grid within a preset time period; wherein the network devices include: servers, 4G wired Ethernet, firewalls, switches and smart meters; wherein the electrical data include: current and voltage; then for each network device, according to the electrical data, calculate the electric energy value, electric quantity differential value, power differential value and line loss change value; then input the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model can extract the electric energy value, electric quantity differential value, power differential value and line loss change value respectively. The data features corresponding to the above electric energy values, electric quantity differential values, power differential values ​​and line loss change values; wherein the above data features include: square root amplitude, skewness, spectrum amplitude and root mean square frequency; then obtain the preset standard data feature range value; wherein the above standard data feature range value includes: standard square root amplitude range value, standard skewness range value, standard spectrum amplitude range value and standard root mean square frequency range value; then compare each of the above data features with the above preset standard data feature range value; finally determine that when any data feature in the above data features exceeds the corresponding preset standard data feature range value, the corresponding network device fails. Therefore, the present invention calculates the data features of electric energy values, electric quantity differential values, power differential values ​​and line loss change values ​​according to the current and voltage of the server, 4G wired Ethernet, firewall, switch and smart meter in the smart grid, compares the data features of each value obtained with the preset standard data feature range, and determines whether the electric energy metering of each network device in the smart grid fails according to the comparison results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention provides a flow chart of an electric energy metering fault detection method according to an embodiment of the present invention.

[0048] Figure 2 It is a structural diagram of an electric energy metering fault detection device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, an electric energy metering fault detection method provided by an embodiment of the present invention includes:

[0051] Step S101: Acquire electrical data of network devices in the smart grid within a preset period of time; wherein the network devices include: servers, 4G wired Ethernet, firewalls, switches and smart meters; wherein the electrical data include: current and voltage;

[0052] Specifically, the current and voltage in each network device are collected through a current sensor and a voltage sensor.

[0053] Step S102: for each network device, according to the electrical data, calculate the electric energy value, the electric quantity differential value, the power differential value and the line loss change value;

[0054] In a preferred embodiment, the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device are calculated based on the electrical data, including:

[0055] Calculating the product of the current, the voltage and the preset time period to obtain the electric energy value;

[0056] Calculating the integral of the product of the current and the voltage in the preset time period to obtain the electrical quantity differential value;

[0057] Calculate the product of the current and the voltage at the preset time within the preset time period to obtain a first power value;

[0058] Calculate the product of the current and the voltage corresponding to the moment before the preset moment to obtain a second power value;

[0059] Calculating the difference between the second power value and the first power value to obtain the power differential value;

[0060] Calculating the product of the current and the voltage to obtain a third power value for a preset period of time;

[0061] Calculate the product of the current in the previous period and the voltage in the previous period to obtain a fourth power value in the previous period;

[0062] The difference between the third power value and the fourth power value is calculated to obtain the line loss change value.

[0063] Specifically, since it is often impossible to directly obtain the electric energy value and the electric quantity differential value when a network device in a smart grid fails, the current and voltage of the network device can be detected and the electric energy value and the electric quantity differential value of the network device can be obtained by calculating the current and voltage.

[0064] Specifically, the greater the power fluctuation, the more serious the line loss, and the greater the line loss change value.

[0065] In this preferred embodiment, the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device are calculated through the electrical data of each network device.

[0066] Step S103: inputting the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency;

[0067] Preferably, when constructing a preset fault detection model, previously recorded electric energy values, electric quantity differential values, power differential values ​​and line loss change values ​​are extracted from the established historical database. Based on the above-mentioned historical data, the data feature extraction operation is performed in the model construction process through three methods: training data evaluation, test data evaluation and algorithm mining data evaluation. Then, the unique data features are determined through the parameter adjustment operation to establish a preset fault detection model.

[0068] In a preferred embodiment, after calculating the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device, the method further includes:

[0069] Compare the above electric energy values, electric quantity differential values, power differential values ​​and line loss change values ​​with the historical electric energy values, historical electric quantity differential values, historical power differential values ​​and historical line loss change values ​​recorded in the historical database;

[0070] The different values ​​are saved in the above historical database to generate a new historical database to record the detection data information of each device in the smart grid.

[0071] Preferably, after obtaining the new historical database, the fault detection model can be optimized and trained using the new historical database, so that the output result of the fault detection model is more accurate.

[0072] Step S104: obtaining preset standard data characteristic range values; wherein the above standard data characteristic range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values ​​and standard root mean square frequency range values;

[0073] Preferably, the preset standard data feature range value is determined based on the data obtained from a large number of tests in advance, and the range value is defined.

[0074] Step S105: comparing each of the above data features with the above preset standard data feature range value;

[0075] Specifically, the square root amplitude corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change of each network device is compared with the standard square root amplitude range value, the skewness is compared with the standard skewness range value, the spectrum amplitude is compared with the standard spectrum amplitude range value, and the root mean square frequency is compared with the standard root mean square frequency range value.

[0076] Step S106: When any one of the data features is determined to exceed a corresponding preset standard data feature range value, the corresponding network device fails.

[0077] Indicatively, if after comparison, it is found that the spectrum amplitude in the power value of the switch exceeds the standard spectrum range value, it means that the switch has a fault.

[0078] In a preferred embodiment, in the above-mentioned determination of the above-mentioned data features, when any data feature exceeds the corresponding preset standard data feature range value, after the corresponding network device fails, it also includes:

[0079] Generate fault information of the failed network device and send the fault information to the user terminal for early warning.

[0080] Specifically, after a network device fails, the failure information is sent to the user terminal of the relevant personnel for early warning, so that the relevant personnel can repair or replace the failed network device to ensure the normal operation of the smart grid.

[0081] In this preferred embodiment, after a network device failure is detected, failure information is generated and sent to the user terminal for early warning.

[0082] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0083] like Figure 2 As shown, an embodiment of the present invention provides an electric energy metering fault detection device, comprising:

[0084] An electrical data acquisition module, an electrical data calculation module, a data feature extraction module, a standard data feature acquisition module, a data feature comparison module, and a fault determination module;

[0085] The electrical data acquisition module is used to acquire electrical data of network devices in the smart grid within a preset period of time; wherein the network devices include: servers, 4G wired Ethernet, firewalls, switches and smart meters; wherein the electrical data include: current and voltage;

[0086] The electrical data calculation module is used to calculate the electric energy value, the electric quantity differential value, the power differential value and the line loss change value for each network device according to the electrical data;

[0087] The data feature extraction module is used to input the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency;

[0088] The standard data feature acquisition module is used to obtain preset standard data feature range values; wherein the standard data feature range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values ​​and standard root mean square frequency range values;

[0089] The data feature comparison module is used to compare each of the data features with the preset standard data feature range value;

[0090] The fault determination module is used to determine that when any data feature among the data features exceeds the corresponding preset standard data feature range value, the corresponding network device fails.

[0091] In a preferred embodiment, the electrical data calculation module comprises: a data calculation unit;

[0092] The above-mentioned data calculation unit includes: calculating the product of the above-mentioned current, the above-mentioned voltage and the above-mentioned preset time period to obtain the above-mentioned electric energy value; calculating the integral of the product of the above-mentioned current and the above-mentioned voltage in the above-mentioned preset time period to obtain the above-mentioned electric quantity differential value; calculating the product of the current and the voltage at the preset moment in the above-mentioned preset time period to obtain the first power value; calculating the product of the current and the voltage corresponding to the previous moment of the above-mentioned preset moment to obtain the second power value; calculating the difference between the above-mentioned second power value and the above-mentioned first power value to obtain the above-mentioned power differential value; calculating the product of the above-mentioned current and the above-mentioned voltage to obtain the third power value of the preset time period; calculating the product of the current of the previous time period and the voltage of the previous time period to obtain the fourth power value of the previous time period; calculating the difference between the above-mentioned third power value and the above-mentioned fourth power value to obtain the above-mentioned line loss change value.

[0093] In another preferred embodiment, it further comprises: a data recording module;

[0094] The above-mentioned data recording module is used to compare the above-mentioned electric energy values, electric quantity differential values, power differential values ​​and the above-mentioned line loss change values ​​with the historical electric energy values, historical electric quantity differential values, historical power differential values ​​and historical line loss change values ​​recorded in the historical database; save the different values ​​in the above-mentioned historical database, and generate a new historical database to record the detection data information of each device in the smart grid.

[0095] In another preferred embodiment, it further comprises: an early warning module;

[0096] The above-mentioned early warning module is used to generate fault information of the failed network device and send the above-mentioned fault information to the user terminal for early warning.

[0097] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without creative work. The above schematic diagram is only an example of an electric energy metering fault detection device, and does not constitute a limitation on an electric energy metering fault detection device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0098] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0099] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements an electric energy metering fault detection method described in any embodiment of the present invention.

[0100] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0101] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0102] The processor referred to herein may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0103] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0104] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0105] Another embodiment of the present invention provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the electric energy metering fault detection method described above in any embodiment of the present invention.

[0106] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0107] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, not only the fault of the smart meter can be detected, but also the fault of other network devices in the smart grid can be detected.

[0108] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting electric energy metering faults, characterized in that: include: Acquire electrical data of network equipment in the smart grid within a preset period of time; wherein the network equipment includes: a server, a 4G wired Ethernet, a firewall, a switch, and a smart meter; wherein the electrical data includes: current and voltage; For each network device, the electric energy value, the electric quantity differential value, the power differential value and the line loss change value are calculated based on the electrical data; Inputting the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency; Obtaining preset standard data feature range values; wherein the standard data feature range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values, and standard root mean square frequency range values; Comparing each of the data features with the preset standard data feature range value; When any one of the data features exceeds a corresponding preset standard data feature range value, the corresponding network device fails.

2. The method for detecting electric energy metering fault according to claim 1, characterized in that: The electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device are calculated based on the electrical data, including: Calculating the product of the current, the voltage and the preset time period to obtain the electric energy value; Calculating the integral of the product of the current and the voltage in the preset time period to obtain the electric quantity differential value; Calculating the product of the current and the voltage at a preset time within the preset time period to obtain a first power value; Calculating the product of the current and the voltage corresponding to the moment before the preset moment to obtain a second power value; Calculating a difference between the second power value and the first power value to obtain the power differential value; Calculating the product of the current and the voltage to obtain a third power value for a preset time period; Calculate the product of the current in the previous period and the voltage in the previous period to obtain a fourth power value in the previous period; The difference between the third power value and the fourth power value is calculated to obtain the line loss change value.

3. The method for detecting electric energy metering fault according to claim 2, characterized in that: After calculating the electric energy value, electric quantity differential value, power differential value and line loss change value corresponding to each network device, it also includes: Comparing the electric energy value, electric quantity differential value, power differential value and line loss change value with the historical electric energy value, historical electric quantity differential value, historical power differential value and historical line loss change value recorded in a historical database; The different values ​​are stored in the historical database, and a new historical database is generated to record the detection data information of each device in the smart grid.

4. The method for detecting electric energy metering fault according to claim 3, characterized in that: In the determining of the data features, when any data feature exceeds a corresponding preset standard data feature range value, after a corresponding network device fails, the method further includes: Generate fault information of the failed network device and send the fault information to the user terminal for early warning.

5. An electric energy metering fault detection device, characterized in that: include: An electrical data acquisition module, an electrical data calculation module, a data feature extraction module, a standard data feature acquisition module, a data feature comparison module, and a fault determination module; The electrical data acquisition module is used to acquire electrical data of network devices in the smart grid within a preset period of time; wherein the network devices include: servers, 4G wired Ethernet, firewalls, switches and smart meters; wherein the electrical data include: current and voltage; The electrical data calculation module is used to calculate the electric energy value, the electric quantity differential value, the power differential value and the line loss change value for each network device according to the electrical data; The data feature extraction module is used to input the electric energy value, electric quantity differential value, power differential value and line loss change value into a preset fault detection model, so that the preset fault detection model extracts data features corresponding to the electric energy value, electric quantity differential value, power differential value and line loss change value respectively; wherein the data features include: root mean square amplitude, skewness, spectrum amplitude and root mean square frequency; The standard data feature acquisition module is used to acquire preset standard data feature range values; wherein the standard data feature range values ​​include: standard square root amplitude range values, standard skewness range values, standard spectrum amplitude range values ​​and standard root mean square frequency range values; The data feature comparison module is used to compare each of the data features with the preset standard data feature range value; The fault determination module is used to determine that when any data feature among the data features exceeds a corresponding preset standard data feature range value, the corresponding network device fails.

6. The electric energy metering fault detection device according to claim 5, characterized in that: The electrical data calculation module includes: a data calculation unit; The data calculation unit includes: calculating the product of the current, the voltage and the preset time period to obtain the electric energy value; calculating the integral of the product of the current and the voltage in the preset time period to obtain the electric quantity differential value; calculating the product of the current and the voltage at a preset time in the preset time period to obtain a first power value; calculating the product of the current and the voltage corresponding to the previous moment of the preset moment to obtain a second power value; calculating the difference between the second power value and the first power value to obtain the power differential value; calculating the product of the current and the voltage to obtain a third power value of the preset time period; calculating the product of the current in the previous time period and the voltage in the previous time period to obtain a fourth power value of the previous time period; calculating the difference between the third power value and the fourth power value to obtain the line loss change value.

7. The electric energy metering fault detection device according to claim 6, characterized in that: Also includes: Data logging module; The data recording module is used to compare the electric energy value, electric quantity differential value, power differential value and line loss change value with the historical electric energy value, historical electric quantity differential value, historical power differential value and historical line loss change value recorded in the historical database; save the different values ​​in the historical database, and generate a new historical database to record the detection data information of each device in the smart grid.

8. The electric energy metering fault detection device according to claim 7, characterized in that: Also includes: Early warning module; The early warning module is used to generate fault information of a failed network device and send the fault information to a user terminal for early warning.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an electric energy metering fault detection method as claimed in any one of claims 1 to 4 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute an electric energy metering fault detection method as claimed in any one of claims 1 to 4.

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