Automatic detection of electric energy meter and electric energy meter fault detection method based on Internet of Things

Abstract

The present invention discloses an automatic detection electric energy meter based on the Internet of Things and an electric energy meter fault detection method, which relates to the field of power supply technology. The electric energy meter includes a shell, a cover plate is arranged on one side of the shell, the shell and the cover plate are connected by a plurality of bolts, an auxiliary mechanism is arranged on the top of the shell, a control main board is arranged on one side of the auxiliary mechanism, and a terminal is arranged on the bottom of the shell; openings are symmetrically provided on both sides of the shell, and a limiting groove is provided on the inner wall of one end of the opening away from the middle of the shell. The present invention arranges an auxiliary mechanism, so that when the temperature inside the electric energy meter is too high, the liquid with a relatively high thermal expansion coefficient, such as an organic solvent in the box body, utilizes the principle of thermal expansion and contraction, and drives the baffle to open by moving the moving rod assembly, thereby enabling the heat inside the electric energy meter to be quickly dissipated, thereby ensuring the normal working environment of each component inside the electric energy meter and improving the service life of the electric energy meter.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply, and in particular to an automatic detection electric energy meter based on the Internet of Things and an electric energy meter fault detection method. Background Art

[0002] With the rapid development of my country's economy and the improvement of people's daily living standards, people have higher-level demands for power supply reliability and quality power supply services. In order to ensure electrical safety and improve service quality, electric energy meters need to provide stable power to electricity users while also providing post-meter line fault identification. The Internet of Things refers to the ubiquitous connection between objects and objects and between objects and people through various types of network access, and the intelligent perception, identification and management of objects and processes; the Internet of Things is an information carrier based on the Internet, traditional telecommunications networks, etc., which allows all ordinary physical objects that can be independently addressed to form an interconnected network.

[0003] For example, Chinese patent CN113791258B discloses an automatic detection electric energy meter based on the Internet of Things, including: a housing; a carrier communication module, the carrier communication module is located inside the housing and movably connected to the housing; a relay, which can improve the user's electricity experience, thereby ensuring the user's electricity demand, so that the quality of electricity service provided to the user is improved. However, for the above electric energy meter, there is a lack of effective real-time fault monitoring and identification mechanism, which leads to the inability to quickly discover and respond when a fault occurs, which may cause greater losses or safety risks. At the same time, the electric energy meter may cause damage to components due to internal overheating during long-term operation. The above electric energy meter lacks an effective overheating protection mechanism and cannot automatically adjust the internal temperature to protect the internal components, thereby reducing the service life of the electric energy meter.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes an automatic detection method of electric energy meters and electric energy meter fault detection based on the Internet of Things to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] According to one aspect of the present invention, there is provided an automatic detection electric energy meter based on the Internet of Things, comprising a shell, a cover plate is provided on one side of the shell, the shell and the cover plate are connected by a plurality of bolts, an auxiliary mechanism is provided on the top of the shell, a control mainboard is provided on one side of the auxiliary mechanism, and a terminal is provided on the bottom of the shell; openings are symmetrically provided on both sides of the shell, and a limit groove is provided on the inner wall of one end of the opening away from the middle of the shell; a metering information acquisition unit, a metering fault detection unit, a metering fault alarm unit and a metering data communication unit are embedded on the control mainboard, and the metering information acquisition unit, the metering fault detection unit, the metering fault alarm unit and the metering data communication unit are all connected to the control mainboard;

[0008] Wherein, the metering information collection unit is used to collect metering information of the electric energy meter through the sensor device;

[0009] A metering fault detection unit, used to analyze the metering fault of the electric energy meter based on the metering information collected from the electric energy meter;

[0010] A metering fault alarm unit, for generating an alarm message when a metering fault is identified;

[0011] The metering data communication unit is used to transmit the metering information, fault detection results and alarm information of the electric energy meter to the monitoring center.

[0012] Furthermore, in order to enable the baffle to be opened by the movement of the moving rod assembly when the internal temperature of the electric energy meter is too high, by utilizing the principle of thermal expansion and contraction through the liquid with a relatively high thermal expansion coefficient such as the organic solvent in the box body, so that the heat inside the electric energy meter can be quickly dissipated, thereby ensuring the normal working environment of the components inside the electric energy meter and improving the service life of the electric energy meter, the auxiliary mechanism includes a box body arranged inside the shell, sleeves are symmetrically arranged on both sides of the box body, a moving rod assembly is arranged inside the sleeve, a rotating rod is arranged at one end of the moving rod assembly, and a baffle matching the opening is arranged at one end of the rotating rod; the moving rod assembly includes a piston plate arranged inside the sleeve, a rod body 1 is arranged on one side of the piston plate, and the rod body 1 One end of the rod body is provided with a second rod; a convex rod is provided on one side of one end of the rod body, and a mounting hole is provided in the middle of the side wall of the other end of the rod body, and a spring connected to the rod body is provided inside the mounting hole, and a plurality of guide columns embedded in the side wall of the first end of the rod body are provided on the outer side of the side wall of the other end of the rod body; the cross-section of the rotating rod is an L-shaped structure, and a sliding hole matching the convex rod is provided on the side wall of one end of the rotating rod, and a rotating shaft connected to the inner wall of the shell is provided in the middle of one side of the rotating rod; a limiting plate matching the limiting groove is provided at the end of the baffle away from the middle of the shell, a sliding groove is provided on one side of the baffle, and the cross-section of the sliding groove is a T-shaped structure, a slide plate is provided inside the slide groove, a convex block is provided on one side of the slide plate, and the convex block is connected to the other end of the rotating rod through a pin shaft.

[0013] Furthermore, in order to realize automatic collection, fault analysis and abnormal alarm of electric energy meter metering information without manual intervention, the efficiency and accuracy of detection are improved. The automatic detection electric energy meter based on the Internet of Things has the characteristics of intelligence, efficiency and reliability, which can improve the management level of electric energy meter, optimize energy utilization and promote sustainable energy development. The metering fault detection unit includes a fault tree network model construction module, a fault pattern recognition module and a fault cause determination module, and the fault tree network model construction module, the fault pattern recognition module and the fault cause determination module are connected in sequence;

[0014] A fault tree network model building module is used to build a metering fault tree network model of the electric energy meter through dependency analysis based on the structural function of the electric energy meter;

[0015] A fault mode recognition module is used to detect metering faults of the electric energy meter based on the metering information of the electric energy meter and based on a multi-fractal algorithm to identify the metering fault mode of the electric energy meter;

[0016] The fault cause determination module is used to determine the fault cause of the electric energy meter according to the metering fault mode of the electric energy meter and in combination with the metering fault tree network model of the electric energy meter.

[0017] Furthermore, the fault tree network model building module includes: a graph model building submodule, a dependency analysis submodule and a fault tree network model building submodule, and the model building submodule, the dependency analysis submodule and the fault tree network model building submodule are connected in sequence;

[0018] The graph model building submodule is used to identify the metering function of the electric energy meter and establish the graph model of the metering component of the electric energy meter based on the graph theory method;

[0019] Identify the metering function of the electric energy meter and establish a graph model of the metering components of the electric energy meter based on graph theory, including:

[0020] According to the metering function of the electric energy meter, identify the metering components of the electric energy meter and the control relationship between the metering components;

[0021] The identified metering components are regarded as nodes in the graph model, and the control relationships between the metering components are regarded as edges in the graph model;

[0022] A graph model of the electric energy meter measurement component is established according to the obtained nodes and edges;

[0023] The dependency analysis submodule is used to determine the dependencies between the metering components according to the graph model of the metering components of the electric energy meter and obtain the dependency analysis results;

[0024] According to the graph model of the electric energy meter measurement components, the dependencies between the measurement components are determined, and the dependency analysis results include:

[0025] Based on the vertices and edges in the graph model of the electric energy meter measurement component, an adjacency matrix of the graph is established;

[0026] Matrix operations are performed using the graph connectivity criterion and the adjacency matrix to determine whether the graph model of the electric energy meter measurement component is connected;

[0027] According to the connectivity judgment criteria, if the graph model is connected, the dependency analysis is performed on the metering components to determine the dependency relationship between the metering components. Otherwise, it is considered that there is no dependency between the metering components.

[0028] A fault tree network model building submodule is used to build a metering fault tree network model of the electric energy meter according to the graph model of the metering components of the electric energy meter and the dependency analysis results;

[0029] According to the graph model of the metering components of the electric energy meter and the results of dependency analysis, the metering fault tree network model of the electric energy meter is established, including:

[0030] Based on the graph model of the energy meter measurement components and the dependency analysis results, determine the top event of the fault tree;

[0031] Identify the bottom events that lead to the top events through literature analysis, and determine the logical relationships between bottom events and between bottom events and top events through logic gates;

[0032] A complete metering fault tree network model of the electric energy meter is constructed based on the determined logical relationship.

[0033] Further, the fault mode identification module includes: a time series construction submodule, a time series segmentation and fitting submodule, a wave function calculation submodule, a characteristic analysis submodule and a fault mode analysis submodule, and the time series construction submodule, the time series segmentation and fitting submodule, the wave function calculation submodule, the characteristic analysis submodule and the fault mode analysis submodule are connected in sequence;

[0034] The time series construction submodule is used to construct the time series data of the metering information according to the metering information of the electric energy meter;

[0035] The time series segmentation and fitting submodule is used to segment the time series data of the measurement information into several non-overlapping segments and perform trend fitting on each segment;

[0036] The wave function calculation submodule is used to calculate the wave function of the entire time series based on the segmentation and fitting results;

[0037] The characteristic analysis submodule is used to analyze the multifractal characteristics of the time series using the obtained wave function. The multifractal characteristics include the calculation of the generalized Hurst index, scale index, singular spectrum and singular index;

[0038] The fault mode analysis submodule is used to compare the multi-fractal characteristics based on the obtained multi-fractal characteristics and utilize a preset abnormal mode database to identify the metering fault mode of the electric energy meter.

[0039] Further, the fault cause determination module includes a feature information extraction submodule, a cause feature extraction submodule, a similarity evaluation submodule and a fault cause screening submodule, and the feature information extraction submodule, the cause feature extraction submodule, the similarity evaluation submodule and the fault cause screening submodule are connected in sequence;

[0040] A feature information extraction submodule is used to extract feature information from the metrology failure mode obtained by multi-fractal algorithm analysis;

[0041] The cause feature extraction submodule is used to identify the cause features related to the cause of the fault from the bottom-level events of the fault tree model;

[0042] The similarity evaluation submodule is used to compare and analyze the extracted fault mode features with the features of the bottom-level events in the fault tree, and to evaluate the similarity between the fault mode features and the features of each bottom-level fault cause.

[0043] The fault cause screening submodule is used to screen out the bottom-level events of the fault tree with the highest matching degree with the current fault mode feature as the fault cause according to the similarity score of feature matching.

[0044] According to another aspect of the present invention, a method for automatically detecting faults of an electric energy meter based on the Internet of Things is provided, comprising the following steps:

[0045] S1. Collecting metering information of electric energy meters through sensor equipment;

[0046] S2. Perform fault analysis on the metering of the electric energy meter based on the metering information collected from the electric energy meter;

[0047] S3, generating an alarm message when a metering failure is identified;

[0048] S4. Transmit the metering information, fault detection results and alarm information of the electric energy meter to the monitoring center.

[0049] The beneficial effects of the present invention are:

[0050] 1. The present invention is provided with an auxiliary mechanism, so that when the internal temperature of the electric energy meter is too high, the liquid with a relatively high thermal expansion coefficient, such as an organic solvent in the box body, utilizes the principle of thermal expansion and contraction, and drives the baffle to open by moving the moving rod assembly, thereby enabling the heat inside the electric energy meter to be quickly dissipated, thereby ensuring the normal working environment of various components inside the electric energy meter and improving the service life of the electric energy meter.

[0051] 2. The present invention realizes automatic collection of metering information, fault analysis and abnormal alarm of electric energy meters through metering information collection units, metering fault detection units and metering fault alarm units, without the need for human intervention, thereby improving the efficiency and accuracy of detection. The automatic detection electric energy meter based on the Internet of Things has the characteristics of intelligence, efficiency and reliability, which can improve the management level of electric energy meters, optimize energy utilization and promote sustainable energy development.

[0052] 3. The present invention realizes automatic detection and diagnosis of metering faults of electric energy meters through a fault pattern recognition module and a fault cause determination module, analyzes metering fault patterns through a multi-fractal algorithm, and realizes rapid and accurate identification of fault causes of electric energy meters in combination with a fault tree network model, thereby improving the efficiency and accuracy of fault diagnosis. By determining the dependencies between metering components, the reliability of fault diagnosis is further improved, missed diagnosis and misdiagnosis are avoided, and the management level of electric energy meters can be improved to ensure the stability and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a structural schematic diagram of an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0055] Figure 2 This is one of the structural schematic diagrams of an automatic detection inside of an electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0056] Figure 3 This is a second structural schematic diagram of an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0057] Figure 4 It is a structural schematic diagram of a housing of an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0058] Figure 5 is a cross-sectional view of an auxiliary mechanism in an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0059] Figure 6 yes Figure 5 A partial enlarged view of the middle A;

[0060] Figure 7It is a structural schematic diagram of a moving rod assembly in an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0061] Figure 8 is a cross-sectional view of a baffle in an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0062] Fig. 9 It is a principle block diagram of a control mainboard, a metering information acquisition unit, a metering fault detection unit, a metering fault alarm unit and a metering data communication unit in an automatic detection electric energy meter based on the Internet of Things according to an embodiment of the present invention;

[0063] Fig.10 The present invention is a flowchart of an automatic detection method of electric energy meter faults based on the Internet of Things according to an embodiment of the present invention.

[0064] In the figure:

[0065] 1. Shell; 101. Opening; 102. Limiting groove; 2. Cover plate; 3. Bolt; 4. Auxiliary mechanism; 401. Box body; 402. Sleeve; 403. Moving rod assembly; 4031. Piston plate; 4032. Rod body 1; 4033. Rod body 2; 4034. Protruding rod; 4035. Mounting hole; 4036. Spring; 4037. Guide column; 404. Rotating rod; 4041. Sliding hole; 4042. Rotating shaft; 405, baffle; 4051, limit plate; 4052, slide; 4053, slide plate; 4054, bump; 4055, pin; 5, control main board; 6, terminal; 7, metering information acquisition unit; 8, metering fault detection unit; 801, fault tree network model building module; 802, fault pattern recognition module; 803, fault cause determination module; 9, metering fault alarm unit; 10, metering data communication unit. DETAILED DESCRIPTION

[0066] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0067] According to an embodiment of the present invention, a method for automatically detecting an electric energy meter and an electric energy meter fault detection method based on the Internet of Things is provided.

[0068] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-9As shown, according to an embodiment of the present invention, an automatic detection electric energy meter based on the Internet of Things is provided, comprising a shell 1, a cover plate 2 is provided on one side of the shell 1, the shell 1 and the cover plate 2 are connected by a plurality of bolts 3, an auxiliary mechanism 4 is provided on the top of the shell 1, a control mainboard 5 is provided on one side of the auxiliary mechanism 4, and a terminal 6 is provided on the bottom end of the shell 1; openings 101 are symmetrically provided on both sides of the shell 1, and a limiting groove 102 is provided on the inner wall of one end of the opening 101 away from the middle of the shell 1; a metering information acquisition unit 7, a metering fault detection unit 8, a metering fault alarm unit 9 and a metering data communication unit 10 are embedded on the control mainboard 5, and the metering information acquisition unit 7, the metering fault detection unit 8, the metering fault alarm unit 9 and the metering data communication unit 10 are all connected to the control mainboard 5;

[0069] The metering information collection unit 7 is used to collect metering information of the electric energy meter through the sensor device;

[0070] It should be noted that commonly used sensors include current sensors, voltage sensors, power sensors, etc.

[0071] The metering fault detection unit 8 is used to analyze the metering fault of the electric energy meter according to the metering information collected from the electric energy meter;

[0072] A metering fault alarm unit 9, for generating an alarm message when a metering fault is identified;

[0073] It should be noted that after identifying a fault mode with a high degree of matching, the metering fault alarm unit 9 will trigger an alarm signal and generate detailed alarm information, including the fault type, time, severity and possible causes.

[0074] The metering data communication unit 10 is used to transmit the metering information, fault detection results and alarm information of the electric energy meter to the monitoring center.

[0075] It should be noted that the electric energy meter and the monitoring center establish a communication connection between the electric energy meter and the monitoring center through a wired or wireless network, and to ensure the security of data transmission, the transmitted data is usually encrypted to prevent the data from being intercepted or tampered with during the transmission process.

[0076] In one embodiment, for the auxiliary mechanism 4, the auxiliary mechanism 4 includes a box body 401 arranged inside the housing 1. In addition, in a specific application, the inside of the box body 401 is filled with a liquid with a high expansion coefficient such as an organic solvent. Sleeves 402 are symmetrically arranged on both sides of the box body 401. A moving rod assembly 403 is arranged inside the sleeve 402. A rotating rod 404 is arranged at one end of the moving rod assembly 403. A baffle 405 matching the opening 101 is arranged at one end of the rotating rod 404. The moving rod assembly 403 includes A piston plate 4031 is arranged inside the sleeve 402, a rod body 1 4032 is arranged on one side of the piston plate 4031, and a rod body 2 4033 is arranged on one end of the rod body 1 4032; a protruding rod 4034 is arranged on one side of one end of the rod body 2 4033, a mounting hole 4035 is opened in the middle of the side wall of the other end of the rod body 2 4033, a spring 4036 connected to the rod body 1 4032 is arranged inside the mounting hole 4035, and a plurality of guide columns embedded in the side wall of one end of the rod body 1 4032 are arranged on the outer side of the side wall of the other end of the rod body 2 4033 4037; the cross section of the rotating rod 404 is an L-shaped structure, and a sliding hole 4041 cooperating with the protruding rod 4034 is provided on the side wall of one end of the rotating rod 404, and a rotating shaft 4042 connected to the inner wall of the shell 1 is provided in the middle of one side of the rotating rod 404; a limiting plate 4051 cooperating with the limiting groove 102 is provided at one end of the baffle 405 away from the middle of the shell 1, and a sliding groove 4052 is provided on one side of the baffle 405, and the cross section of the sliding groove 4052 is a T-shaped structure, and a sliding plate 4053 is provided inside the sliding groove 4052, A protrusion 4054 is provided on one side of the slide plate 4053, and the protrusion 4054 is connected to the other end of the rotating rod 404 through a pin shaft 4055. Therefore, when the internal temperature of the electric energy meter is too high, the liquid with a relatively high thermal expansion coefficient, such as an organic solvent inside the box body 401, can use the principle of thermal expansion and contraction to drive the baffle 405 to open through the movement of the moving rod assembly 403, so that the heat inside the electric energy meter can be quickly dissipated, thereby ensuring the normal working environment of various components inside the electric energy meter and improving the service life of the electric energy meter.

[0077] In addition, the working principle of the auxiliary mechanism 4 is as follows: when the temperature inside the electric energy meter rises, the organic solvent inside the box body 401, using the principle of thermal expansion and contraction, increases the volume of the organic solvent, thereby driving the piston plate 4031 to move, and then driving the rod body 1 4032 to move, thereby driving the rod body 2 4033 to move, and under the action of the convex rod 4034, driving the rotating rod 404 to rotate around the rotating axis 4042 as the center, and then driving the slide plate 4053 to slide inside the slide groove 4052 through the other end of the rotating rod 404, thereby driving the baffle 405 to move, so that the baffle 405 is opened from the opening 101, so that the heat in the electric energy meter can flow to the outside, thereby realizing the heat dissipation of the electric energy meter. When the temperature inside the electric energy meter drops, the volume of the organic solvent shrinks, causing the baffle 405 to enter the opening 101. Due to the cooperation between the limit groove 102 and the limit plate 4051, the baffle 405 can seal the electric energy meter. Then, with the cooperation of the spring 4036 and the guide column 4037, when the temperature of the organic solvent drops to a very low level and the limit groove 102 and the limit plate 4051 fit together, and the rod body 2 4033 with the cooperation of the limit groove 102 and the limit plate 4051, will not hinder the piston plate 4031 and the rod body 1 4032 from continuing to move, thereby ensuring the stability of the auxiliary mechanism 4.

[0078] In one embodiment, for the metering fault detection unit 8, the metering fault detection unit 8 includes a fault tree network model construction module 801, a fault mode recognition module 802 and a fault cause determination module 803, and the fault tree network model construction module 801, the fault mode recognition module 802 and the fault cause determination module 803 are connected in sequence;

[0079] The fault tree network model building module 801 is used to build a metering fault tree network model of the electric energy meter through dependency analysis based on the structural function of the electric energy meter;

[0080] As a preferred implementation, the fault tree network model building module 801 includes: a graph model building submodule, a dependency analysis submodule and a fault tree network model building submodule, and the model building submodule, the dependency analysis submodule and the fault tree network model building submodule are connected in sequence;

[0081] The graph model building submodule is used to identify the metering function of the electric energy meter and establish the graph model of the metering component of the electric energy meter based on the graph theory method;

[0082] Specifically, the metering function of the electric energy meter is identified, and a graph model of the electric energy meter metering component is established based on graph theory, including:

[0083] According to the metering function of the electric energy meter, identify the metering components of the electric energy meter and the control relationship between the metering components;

[0084] It should be noted that the metering functions of the electric energy meter include the measurement of voltage, current, power and energy. Based on the measurement of the above functions, the core components required to realize these metering functions are identified, including current transformers, voltage transformers, analog-to-digital converters, microcontrollers, etc.; the data flow between components is analyzed, for example, how the analog signals captured by the current and voltage sensors are transmitted to the analog-to-digital converter, and the control logic between components is understood, such as how the microcontroller sends instructions to control the operation of other components (such as data acquisition frequency, data transmission, etc.).

[0085] The identified metering components are regarded as nodes in the graph model, and the control relationships between the metering components are regarded as edges in the graph model;

[0086] It should be noted that all identified metering components are considered as nodes of the graph model. For example, current transformers, voltage transformers, microprocessors (or microcontrollers), and analog-to-digital converters (ADCs) should be identified as separate nodes.

[0087] Based on the control relationship between components, the edges in the graph are determined. For example, if component A provides data or control signals to component B, a directed edge is drawn between the two nodes, with the arrow pointing from A to B; for example, the data flow from the current transformer to the microprocessor will be represented as an edge from the current transformer node to the microprocessor node.

[0088] A graph model of the electric energy meter measurement component is established according to the obtained nodes and edges;

[0089] It should be noted that the determined nodes and edges are organized into a graph structure; nodes are represented as circles or boxes in the graph, edges are represented as lines connecting nodes, and directed edges are represented by arrows to indicate control directions.

[0090] The dependency analysis submodule is used to determine the dependencies between the metering components according to the graph model of the metering components of the electric energy meter and obtain the dependency analysis results;

[0091] Specifically, according to the graph model of the electric energy meter measurement components, the dependencies between the measurement components are determined, and the dependency analysis results obtained include:

[0092] Based on the vertices and edges in the graph model of the electric energy meter measurement component, an adjacency matrix of the graph is established;

[0093] It should be noted that in the adjacency matrix, the rows and columns of the matrix represent the vertices in the graph. If there is an edge between vertex i and vertex j, then the position in the i-th row and j-th column of the matrix (and the j-th row and i-th column if it is an undirected graph) is marked as 1, otherwise it is marked as 0.

[0094] Matrix operations are performed using the graph connectivity criterion and the adjacency matrix to determine whether the graph model of the electric energy meter measurement component is connected;

[0095] Specifically, using the connectivity judgment criteria of the graph and the adjacency matrix to perform matrix operations, determining whether the graph model of the electric energy meter measurement component is connected includes the following steps:

[0096] Based on the obtained adjacency matrix, calculate the power of the adjacency matrix. Specifically, calculate the 2nd power, 3rd power, and up to the Nth power, where N is the number of vertices in the graph, by matrix multiplication; for each power calculation, check all non-diagonal elements of the result matrix;

[0097] If there exists a positive integer k (k≤N) such that all off-diagonal elements in the k-th power of the adjacency matrix are greater than 0, then the graph is considered connected. Any two vertices in the graph can reach each other through a series of edges. If for all 1≤k≤N, there are off-diagonal elements in the k-th power of the adjacency matrix that are 0, then the graph is not connected.

[0098] According to the connectivity judgment criteria, if the graph model is connected, the dependency analysis is performed on the metering components to determine the dependency relationship between the metering components. Otherwise, it is considered that there is no dependency between the metering components.

[0099] It should be noted that, in the energy meter measurement components, one component may rely on another component to obtain energy, signals or data.

[0100] Dependency analysis includes direct dependencies and indirect dependencies. Using graph theory algorithms, such as depth-first search (DFS) or breadth-first search (BFS), all possible paths from one node (component) to another can be explored to determine the direct and indirect dependencies between components.

[0101] A fault tree network model building submodule is used to build a metering fault tree network model of the electric energy meter according to the graph model of the metering components of the electric energy meter and the dependency analysis results;

[0102] Specifically, according to the graph model of the metering components of the electric energy meter and the dependency analysis results, the metering fault tree network model of the electric energy meter is established, including:

[0103] Based on the graph model of the energy meter measurement components and the dependency analysis results, determine the top event of the fault tree;

[0104] It should be noted that the top event is described as a state in which the metering system of the electric energy meter fails completely or the measurement data is seriously inaccurate; for example, "the metering function of the electric energy meter fails completely" or "the measurement error of the electric energy meter exceeds the allowable range"; based on the constructed graph model and dependency analysis results, by evaluating which metering components' failures will affect the core metering functions of the electric energy meter, the components that have the greatest impact on the metering performance of the electric energy meter are identified, and the failure of these components may directly lead to top events.

[0105] Identify the bottom events that lead to the top events through literature analysis, and determine the logical relationships between bottom events and between bottom events and top events through logic gates;

[0106] It should be noted that the literature analysis method is to identify the bottom events that may cause the top events by reviewing relevant technical and scientific literature, historical failure data, manufacturer documents, and operation and maintenance manuals. These bottom events are usually specific component failures, operating errors, or external factors (such as environmental impacts). In fault tree analysis, logic gates are used to represent the relationship between faults. Commonly used logic gates include AND gates, OR gates, NOT gates, etc. Based on the characteristics of bottom events and their contribution to top events, the logical relationship between bottom events and between bottom events and top events is determined. For example, if the simultaneous failure of two components will cause the metering function to fail, then the two bottom events should be connected using an AND gate.

[0107] A complete metering fault tree network model of the electric energy meter is constructed based on the determined logical relationship.

[0108] It should be noted that by utilizing the identified bottom events and determined logical relationships, the fault tree model is constructed, starting from the top event and branching out different logical paths downward until all bottom events are included.

[0109] The fault mode identification module 802 is used to detect the metering fault of the electric energy meter based on the metering information of the electric energy meter and the multi-fractal algorithm to identify the metering fault mode of the electric energy meter;

[0110] As a preferred implementation, the fault mode identification module 802 includes: a time series construction submodule, a time series segmentation and fitting submodule, a wave function calculation submodule, a characteristic analysis submodule and a fault mode analysis submodule, and the time series construction submodule, the time series segmentation and fitting submodule, the wave function calculation submodule, the characteristic analysis submodule and the fault mode analysis submodule are connected in sequence;

[0111] The time series construction submodule is used to construct the time series data of the metering information according to the metering information of the electric energy meter;

[0112] It should be noted that we first determine the time range to be analyzed, such as a day, a week, a month, or longer, assign a precise timestamp to each data point, ensure that each metering data is accurately associated with its corresponding collection time, and arrange the metering data according to the timestamp to form a chronological sequence.

[0113] The time series segmentation and fitting submodule is used to segment the time series data of the measurement information into several non-overlapping segments and perform trend fitting on each segment;

[0114] Specifically, the method for dividing the time series data of the measurement information into a number of non-overlapping segments and performing trend fitting on each segment includes the following steps:

[0115] Determine the segmentation strategy based on the characteristics of the data and the purpose of the analysis. For example, you can segment the data by natural time periods (such as a day or a week), or dynamically segment the data based on key points of data change (such as significant trends or pattern changes).

[0116] Use linear regression model to fit the trend of each data segment;

[0117] Analyze the fitting results of each segment to identify and explain the trend changes of the data. For example, linear growth, cyclical fluctuations, trend mutation points, etc.; compare the trend differences between different segments and analyze the overall change pattern and cyclical characteristics of time series data.

[0118] The wave function calculation submodule is used to calculate the wave function of the entire time series based on the segmentation and fitting results;

[0119] Specifically, the deviation between the actual data point and the fitting trend in each segment is quantified by calculating the sum of the squares of the distances between each data point and the fitting curve, and the calculation result is used as the fluctuation function.

[0120] The characteristic analysis submodule is used to analyze the multifractal characteristics of the time series using the obtained wave function. The multifractal characteristics include the calculation of the generalized Hurst index, scale index, singular spectrum and singular index;

[0121] It should be noted that the generalized Hurst index is an indicator that describes the long-term memory of a time series. In multifractal analysis, the generalized Hurst index H(q) analyzes different q-order moments to explore the multifractal characteristics of the data. Using the wave function, the generalized Hurst index can be estimated by adjusting the time scale and calculating different q-order statistics (for example, the cumulative distribution of the q-order difference of the time series). The change of H(q) with q can reveal the long-term correlation under different fluctuation amplitudes.

[0122] The scaling exponent τ(q) describes the self-similarity of a time series at different scales. It is associated with the generalized Hurst exponent and can be used to analyze the fractal properties of a time series. The scaling exponent τ(q) is usually calculated by the slope of the volatility function on a log-log scale, reflecting the scaling behavior of volatility at different scales.

[0123] The singular index describes the local Holder index of the singular (extreme or atypical) part of the time series, which reflects the local variability of the data in different parts. The singular spectrum is a tool used to analyze the distribution of the singular index. It provides a perspective to understand the richness and complexity of various singularities in time series. The singular spectrum and singular index can be estimated by utilizing the so-called multifractal spectrum analysis method, which usually involves the Legendre transformation of the fractal measure derived from the wave function.

[0124] The fault mode analysis submodule is used to compare the multi-fractal characteristics based on the obtained multi-fractal characteristics and utilize a preset abnormal mode database to identify the metering fault mode of the electric energy meter.

[0125] Specifically, according to the obtained multi-fractal characteristics, using a pre-set abnormal mode database to compare the multi-fractal characteristics, identifying the metering failure mode of the electric energy meter includes the following steps:

[0126] Collect a series of known metrological failure modes and their corresponding multifractal properties, including generalized Hurst exponent, scaling exponent, singular spectrum and singular exponent;

[0127] Through experiments or historical data analysis, identify the multifractal characteristics of different types of failure modes; each failure mode should correspond to a specific set of multifractal parameters, and establish an abnormal mode database;

[0128] Compare the multifractal characteristics of the energy meter to be diagnosed with the characteristics in the abnormal pattern database;

[0129] According to the results of comparative analysis, the fault mode closest to the multifractal characteristics of the electric energy meter to be diagnosed is identified. If the characteristics matching the known fault mode are found, it can be determined that the electric energy meter has the corresponding metering fault.

[0130] The fault cause determination module 803 is used to determine the fault cause of the electric energy meter according to the metering fault mode of the electric energy meter and in combination with the metering fault tree network model of the electric energy meter.

[0131] As a preferred implementation, the fault cause determination module 803 includes a feature information extraction submodule, a cause feature extraction submodule, a similarity evaluation submodule and a fault cause screening submodule, and the feature information extraction submodule, the cause feature extraction submodule, the similarity evaluation submodule and the fault cause screening submodule are connected in sequence;

[0132] The feature information extraction submodule is used to extract feature information from the measurement failure mode obtained by multi-fractal algorithm analysis; including the frequency of abnormal fluctuations, amplitude changes, long-term trend deviations, etc.

[0133] It should be noted that features related to abnormal fluctuations are extracted from the analysis results, such as the frequency and amplitude changes of fluctuations, and the deviations from long-term trends are analyzed, such as changes in the generalized Hurst exponent, to identify persistent failure modes or systematic deviations. Features that characterize extreme behaviors or atypical changes in the data are extracted through singular spectra and singular indices.

[0134] The cause feature extraction submodule is used to identify the cause features related to the cause of the fault from the bottom-level events of the fault tree model;

[0135] It should be noted that the underlying events are classified according to their nature, such as operating errors, component failures, external factors, etc., to determine which attributes or conditions can be used as characteristics of the cause of the failure, such as the failure mode of a specific component, abnormal operating conditions, changes in environmental conditions, etc. For each underlying event, a detailed analysis is performed to extract the characteristic information directly related to the cause of the failure.

[0136] The similarity evaluation submodule is used to compare and analyze the extracted fault mode features with the features of the bottom-level events in the fault tree, and to evaluate the similarity between the fault mode features and the features of each bottom-level fault cause.

[0137] It should be noted that the extracted fault mode features and the features of the bottom-level events of the fault tree are feature vectorized to construct feature vectors, all feature vectors are standardized, and then the cosine similarity evaluation method is used to calculate the similarity between each pair of fault mode feature vectors and the bottom-level event feature vectors of the fault tree, and a score is assigned to the similarity calculated for each pair of feature vectors. The higher the score, the higher the similarity.

[0138] The fault cause screening submodule is used to screen out the bottom-level events of the fault tree with the highest matching degree with the current fault mode feature as the fault cause according to the similarity score of feature matching.

[0139] It should be noted that, based on the similarity score of feature matching, selecting the bottom-level event of the fault tree with the highest matching degree with the current fault mode feature as the fault cause includes the following steps:

[0140] After completing the similarity calculation between all fault mode feature vectors and the bottom event feature vectors of the fault tree, the obtained scores are summarized to form a similarity score list;

[0141] According to the similarity score, sort the score list from high to low, so that you can intuitively see which underlying event features are closest to the features of the current fault mode;

[0142] From the sorted list, select the item with the highest similarity score as the most likely cause of the failure.

[0143] like Fig.10 According to another aspect of the present invention, a method for automatically detecting faults of an electric energy meter based on the Internet of Things is provided, comprising the following steps:

[0144] S1. Collecting metering information of electric energy meters through sensor equipment;

[0145] S2. Perform fault analysis on the metering of the electric energy meter based on the metering information collected from the electric energy meter;

[0146] S3, generating an alarm message when a metering failure is identified;

[0147] S4. Transmit the metering information, fault detection results and alarm information of the electric energy meter to the monitoring center.

[0148] In order to facilitate understanding of the above technical solutions of the present invention, the working principle or operation mode of the present invention in the actual process is described in detail below.

[0149] In actual application, when the electric energy meter is working and the temperature inside the electric energy meter rises, the principle of thermal expansion and contraction will be used. The inside of the electric energy meter can be brought into contact with the outside world through the auxiliary mechanism 4 (the working principle of the auxiliary mechanism 4 is as described above), so that the internal hot air can flow out when the electric energy meter is in a high temperature state, and the effect of automatic heat dissipation can be achieved to ensure the normal operation of the electric energy meter. At the same time, the metering information of the electric energy meter can be collected by the metering information collection unit 7, and the metering fault detection unit 8 can perform fault analysis on the metering of the electric energy meter based on the collected metering information of the electric energy meter. When a metering fault is identified, an alarm message is generated by the metering fault alarm unit 9, and the metering information, fault detection results and alarm information of the electric energy meter are transmitted to the monitoring center by the metering data communication unit 10.

[0150] In summary, with the aid of the above technical scheme of the present invention, the present invention sets an auxiliary mechanism 4, so that when the internal temperature of the electric energy meter is too high, the liquid with a relatively high thermal expansion coefficient, such as the organic solvent inside the box body 401, utilizes the principle of thermal expansion and contraction, and drives the baffle 405 to open by moving the moving rod assembly 403, so that the heat inside the electric energy meter can be quickly dissipated, thereby ensuring the normal working environment of various components inside the electric energy meter and improving the service life of the electric energy meter; the present invention realizes automatic collection of metering information, fault analysis and abnormal alarm of the electric energy meter through the metering information collection unit 7, the metering fault detection unit 8 and the metering fault alarm unit 9, without the need for manual intervention, thereby improving the detection efficiency. The automatic detection electric energy meter based on the Internet of Things has the characteristics of intelligence, high efficiency and reliability, which can improve the management level of the electric energy meter, optimize energy utilization and promote sustainable energy development. The present invention realizes automatic detection and diagnosis of metering faults of the electric energy meter through a fault pattern recognition module and a fault cause determination module, analyzes the metering fault pattern through a multi-fractal algorithm, and combines the fault tree network model to realize rapid and accurate identification of the cause of the electric energy meter fault, thereby improving the efficiency and accuracy of fault diagnosis, and further improving the reliability of fault diagnosis by determining the dependency between metering components, thereby avoiding missed diagnosis and misdiagnosis, and can improve the management level of the electric energy meter and ensure the stability and reliability of the power supply.

[0151] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An automatic detection electric energy meter based on the Internet of Things, characterized in that: It comprises a shell (1), a cover plate (2) is arranged on one side of the shell (1), the shell (1) and the cover plate (2) are connected via a plurality of bolts (3), an auxiliary mechanism (4) is arranged on the top of the shell (1), a control main board (5) is arranged on one side of the auxiliary mechanism (4), and a terminal (6) is arranged on the bottom end of the shell (1); Openings (101) are symmetrically provided on both sides of the shell (1), and a limiting groove (102) is provided on the inner wall of one end of the opening (101) away from the middle of the shell (1); The control main board (5) is embedded with a metering information collection unit (7), a metering fault detection unit (8), a metering fault alarm unit (9) and a metering data communication unit (10), and the metering information collection unit (7), the metering fault detection unit (8), the metering fault alarm unit (9) and the metering data communication unit (10) are all connected to the control main board (5); The metering information acquisition unit (7) is used to acquire metering information of the electric energy meter through a sensor device; The metering fault detection unit (8) is used to perform fault analysis on the metering of the electric energy meter based on the metering information collected from the electric energy meter; The metering failure alarm unit (9) is used to generate alarm information when a metering failure is identified; The metering data communication unit (10) is used to transmit metering information, fault detection results and alarm information of the electric energy meter to a monitoring center; The auxiliary mechanism (4) comprises a box body (401) arranged inside the shell (1), sleeves (402) are symmetrically arranged on both sides of the box body (401), a moving rod assembly (403) is arranged inside the sleeve (402), a rotating rod (404) is arranged at one end of the moving rod assembly (403), and a baffle (405) matching the opening (101) is arranged at one end of the rotating rod (404); The moving rod assembly (403) comprises a piston plate (4031) arranged inside the sleeve (402), a rod body 1 (4032) is arranged on one side of the piston plate (4031), and a rod body 2 (4033) is arranged at one end of the rod body 1 (4032); A protruding rod (4034) is provided on one side of one end of the second rod body (4033); a mounting hole (4035) is provided in the middle of the side wall of the other end of the second rod body (4033); a spring (4036) connected to the first rod body (4032) is provided inside the mounting hole (4035); and a plurality of guide posts (4037) embedded in the side wall of one end of the first rod body (4032) are provided on the outer side of the side wall of the other end of the second rod body (4033); The cross section of the rotating rod (404) is an L-shaped structure, and a sliding hole (4041) matching with the protruding rod (4034) is provided on a side wall at one end of the rotating rod (404), and a rotating shaft (4042) connected to the inner wall of the housing (1) is provided in the middle of one side of the rotating rod (404); A limiting plate (4051) matching with the limiting groove (102) is arranged at one end of the baffle (405) away from the middle of the shell (1); a sliding groove (4052) is provided on one side of the baffle (405); and the cross section of the sliding groove (4052) is a T-shaped structure; a sliding plate (4053) is arranged inside the sliding groove (4052); a protrusion (4054) is arranged on one side of the sliding plate (4053); and the protrusion (4054) is connected to the other end of the rotating rod (404) via a pin shaft (4055).

2. According to the automatic detection electric energy meter based on the Internet of Things according to claim 1, it is characterized in that: The metering fault detection unit (8) comprises a fault tree network model construction module (801), a fault mode identification module (802) and a fault cause determination module (803), and the fault tree network model construction module (801), the fault mode identification module (802) and the fault cause determination module (803) are connected in sequence; The fault tree network model construction module (801) is used to construct a metering fault tree network model of the electric energy meter through dependency analysis based on the structural function of the electric energy meter; The fault mode identification module (802) is used to perform metering fault detection on the electric energy meter based on the metering information of the electric energy meter and based on the multi-fractal algorithm to identify the metering fault mode of the electric energy meter; The fault cause determination module (803) is used to determine the fault cause of the electric energy meter according to the metering fault mode of the electric energy meter and in combination with the metering fault tree network model of the electric energy meter.

3. The automatic detection electric energy meter based on the Internet of Things according to claim 2 is characterized in that: The fault tree network model construction module (801) comprises: a graph model construction submodule, a dependency analysis submodule and a fault tree network model establishment submodule, and the model construction submodule, the dependency analysis submodule and the fault tree network model establishment submodule are connected in sequence; The graph model building submodule is used to identify the metering function of the electric energy meter and to establish a graph model of the metering component of the electric energy meter based on graph theory; The method of identifying the metering function of the electric energy meter and establishing a graph model of the metering component of the electric energy meter based on the graph theory method includes: According to the metering function of the electric energy meter, identify the metering components of the electric energy meter and the control relationship between the metering components; The identified metering components are regarded as nodes in the graph model, and the control relationships between the metering components are regarded as edges in the graph model; A graph model of the electric energy meter measurement component is established according to the obtained nodes and edges; The dependency analysis submodule is used to determine the dependencies between the metering components according to the graph model of the metering components of the electric energy meter, and obtain the dependency analysis result; Determining the dependencies between the metering components according to the graph model of the metering components of the electric energy meter and obtaining the dependency analysis results includes: Based on the vertices and edges in the graph model of the electric energy meter measurement component, an adjacency matrix of the graph is established; Matrix operations are performed using the graph connectivity criterion and the adjacency matrix to determine whether the graph model of the electric energy meter measurement component is connected; According to the connectivity judgment criteria, if the graph model is connected, the dependency analysis is performed on the metering components to determine the dependency relationship between the metering components. Otherwise, it is considered that there is no dependency between the metering components. The fault tree network model establishment submodule is used to establish a metering fault tree network model of the electric energy meter according to the graph model of the metering components of the electric energy meter and the dependency analysis results; The method of establishing a metering fault tree network model of the electric energy meter according to the graph model of the metering component of the electric energy meter and the dependency analysis result comprises: Based on the graph model of the energy meter measurement components and the dependency analysis results, determine the top event of the fault tree; Identify the bottom events that lead to the top events through literature analysis, and determine the logical relationships between bottom events and between bottom events and top events through logic gates; A complete metering fault tree network model of the electric energy meter is constructed based on the determined logical relationship.

4. The automatic detection electric energy meter based on the Internet of Things according to claim 3 is characterized in that: The fault mode identification module (802) comprises: a time series construction submodule, a time series segmentation and fitting submodule, a wave function calculation submodule, a characteristic analysis submodule and a fault mode analysis submodule, and the time series construction submodule, the time series segmentation and fitting submodule, the wave function calculation submodule, the characteristic analysis submodule and the fault mode analysis submodule are connected in sequence; The time series construction submodule is used to construct time series data of the metering information according to the metering information of the electric energy meter; The time series segmentation and fitting submodule is used to segment the time series data of the measurement information into a number of non-overlapping segments and perform trend fitting on each segment; The wave function calculation submodule is used to calculate the wave function of the entire time series according to the segmentation and fitting results; The characteristic analysis submodule is used to analyze the multifractal characteristics of the time series using the obtained wave function, and the multifractal characteristics include calculating the generalized Hurst index, the scale index, the singular spectrum and the singular index; The fault mode analysis submodule is used to compare the multi-fractal characteristics according to the obtained multi-fractal characteristics using a preset abnormal mode database to identify the metering fault mode of the electric energy meter.

5. The automatic detection electric energy meter based on the Internet of Things according to claim 4 is characterized in that: The fault cause determination module (803) comprises a feature information extraction submodule, a cause feature extraction submodule, a similarity evaluation submodule and a fault cause screening submodule, and the feature information extraction submodule, the cause feature extraction submodule, the similarity evaluation submodule and the fault cause screening submodule are connected in sequence; The characteristic information extraction submodule is used to extract characteristic information from the metering fault mode obtained by multi-fractal algorithm analysis; The cause feature extraction submodule is used to identify cause features related to the cause of the fault from the bottom-level events of the fault tree model; The similarity evaluation submodule is used to compare and analyze the extracted fault mode features with the features of the bottom-level events of the fault tree, and evaluate the similarity between the fault mode features and the features of each bottom-level fault cause; The fault cause screening submodule is used to screen out the bottom-level event of the fault tree with the highest matching degree with the current fault mode feature as the fault cause according to the similarity score of feature matching.

6. A method for automatically detecting faults of electric energy meters based on the Internet of Things, used to implement the automatic detection of faults of electric energy meters based on the Internet of Things as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Collecting metering information of electric energy meters through sensor equipment; S2. Perform fault analysis on the metering of the electric energy meter based on the metering information collected from the electric energy meter; S3, generating an alarm message when a metering failure is identified; S4. Transmit the metering information, fault detection results and alarm information of the electric energy meter to the monitoring center.

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