Intelligent electric energy meter fault assessment method and device

Abstract

This invention belongs to the field of fault assessment technology and discloses a method and apparatus for fault assessment of smart meters. The method includes: acquiring fault data information of the smart meter; constructing an evaluation matrix based on the fault data information; preprocessing the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; determining the weights corresponding to each fault type based on the reference matrix, and performing normalized weighted processing on the reference matrix based on the weights to obtain a target matrix; and determining the evaluation index of the smart meter under different dimensions based on the target matrix. By using the fault type of the smart meter as the evaluation index, the faults of the smart meter are evaluated from different dimensions, making the fault assessment more comprehensive and improving the accuracy of the assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of fault assessment, and in particular to a method and device for assessing faults of a smart electric energy meter. Background Art

[0002] Smart electricity meters are important equipment in smart grids. With the development of smart grids, the number of installed smart electricity meters has increased, and corresponding failures have occurred frequently. Every year, a large number of smart electricity meters fail in the power grid and need to be dismantled for repair. The failure forms are diverse and complex and sudden.

[0003] Currently, for smart meters that are dismantled for repair, it is usually determined whether they need repair by evaluating them. However, the current fault assessment method does not take into account various situations such as faults of smart meters and expiration of their service life, resulting in the fault assessment method being one-sided and inaccurate.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a method and device for evaluating faults of a smart electric energy meter, aiming to solve the technical problem that current fault evaluation is one-sided and inaccurate.

[0006] To achieve the above object, the present invention provides a smart energy meter fault assessment method, the smart energy meter fault assessment method comprising the following steps:

[0007] Obtain fault data information of smart electric energy meters;

[0008] Constructing an evaluation matrix according to the fault data information, wherein the evaluation matrix includes evaluation values ​​of each fault type;

[0009] Preprocessing the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix;

[0010] Determine the weight corresponding to each fault type based on the reference matrix, and perform normalized weighted processing on the reference matrix based on the weight to obtain a target matrix;

[0011] The evaluation index of the smart electric energy meter in different dimensions is determined according to the target matrix.

[0012] In some embodiments, the preprocessing includes standardization and normalization, and the preprocessing of the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix includes:

[0013] Obtaining the index values ​​in the evaluation matrix, and calculating the standard values ​​based on the index values;

[0014] constructing a standardized matrix according to the standard values;

[0015] The standardized matrix is ​​normalized to obtain a reference matrix corresponding to the evaluation matrix.

[0016] In some embodiments, determining the weight corresponding to each fault type based on the reference matrix, and performing normalized weighted processing on the reference matrix based on the weight to obtain a target matrix includes:

[0017] Calculate the entropy value of each fault type based on the reference matrix;

[0018] Determine a weight corresponding to each fault type according to the entropy value of each fault type, wherein the sum of the weights corresponding to each fault type is 1;

[0019] The reference matrix is ​​normalized and weighted according to the weights corresponding to the fault types to obtain a target matrix.

[0020] In some embodiments, determining the evaluation index of the smart electric energy meter in different dimensions according to the target matrix includes:

[0021] Determine a first ideal solution and a second ideal solution based on a preset ideal system, wherein the first ideal solution corresponds to a solution in which the indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which the indicator reaches a worst value;

[0022] The evaluation index of the smart electric energy meter in different dimensions is determined according to the target matrix, the first ideal solution and the second ideal solution.

[0023] In some embodiments, determining the evaluation index of the smart electric energy meter in different dimensions according to the target matrix, the first ideal solution, and the second ideal solution includes:

[0024] Calculating a first weighted distance based on the target matrix and the first ideal solution;

[0025] Calculating a second weighted distance according to the target matrix and the second ideal solution;

[0026] An evaluation index of the smart electric energy meter in a first dimension is determined according to the first weighted distance and the second weighted distance.

[0027] In some embodiments, determining the evaluation index of the smart electric energy meter in different dimensions according to the target matrix, the first ideal solution, and the second ideal solution includes:

[0028] Calculate a first correlation coefficient according to the target matrix, the first ideal solution and preset parameters;

[0029] Calculate a second correlation coefficient according to the target matrix, the second ideal solution and preset parameters;

[0030] determining a first degree of association based on the first correlation coefficient, and determining a second degree of association based on the second correlation coefficient;

[0031] An evaluation index of the smart electric energy meter in a second dimension is determined according to the first correlation degree and the second correlation degree.

[0032] In addition, to achieve the above-mentioned purpose, the present invention further proposes a smart electric energy meter fault assessment device, the smart electric energy meter fault assessment device comprising:

[0033] An acquisition module, used to acquire fault data information of a smart electric energy meter;

[0034] A construction module, used to construct an evaluation matrix according to the fault data information, wherein the evaluation matrix includes evaluation values ​​of each fault type;

[0035] The construction module is used to preprocess the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix;

[0036] The construction module is used to determine the weight corresponding to each fault type based on the reference matrix, and perform normalized weighted processing on the reference matrix based on the weight to obtain a target matrix;

[0037] A processing module is used to determine the evaluation index of the smart electric energy meter in different dimensions according to the target matrix.

[0038] In some embodiments, the construction module is used to obtain the index value in the evaluation matrix and calculate the standard value based on the index value;

[0039] constructing a standardized matrix according to the standard values;

[0040] The standardized matrix is ​​normalized to obtain a reference matrix corresponding to the evaluation matrix.

[0041] In some embodiments, the building module is used to calculate the entropy value of each fault type based on the reference matrix;

[0042] Determine a weight corresponding to each fault type according to the entropy value of each fault type, wherein the sum of the weights corresponding to each fault type is 1;

[0043] The reference matrix is ​​normalized and weighted according to the weights corresponding to the fault types to obtain a target matrix.

[0044] In some embodiments, the processing module is used to determine a first ideal solution and a second ideal solution based on a preset ideal system, wherein the first ideal solution corresponds to a solution in which an indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which an indicator reaches a worst value;

[0045] The evaluation index of the smart electric energy meter in different dimensions is determined according to the target matrix, the first ideal solution and the second ideal solution.

[0046] The present invention obtains fault data information of a smart electric energy meter; constructs an evaluation matrix according to the fault data information; pre-processes the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; determines the weights corresponding to each fault type based on the reference matrix, and performs normalized weighted processing on the reference matrix based on the weight to obtain a target matrix; determines the evaluation index of the smart electric energy meter in different dimensions according to the target matrix, and uses the fault type of the smart electric energy meter as an evaluation index in the above manner to evaluate the fault of the smart electric energy meter from different dimensions, thereby making the fault evaluation more comprehensive and improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a flow chart of a first embodiment of a method for evaluating faults of a smart electric energy meter according to the present invention;

[0048] Figure 2 A schematic diagram of a flow chart of a second embodiment of a method for fault assessment of a smart electric energy meter according to the present invention;

[0049] Figure 3 This is a structural block diagram of the first embodiment of the intelligent electric energy meter fault assessment device of the present invention.

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0052] The embodiment of the present invention provides a method for evaluating faults of a smart electric energy meter. Figure 1 , Figure 1 The figure is a flow chart of a first embodiment of a method for fault assessment of a smart electric energy meter according to the present invention.

[0053] In this embodiment, the smart energy meter fault assessment method includes the following steps:

[0054] Step S10: Acquire fault data information of the smart electric energy meter.

[0055] In this embodiment, the executor of this embodiment is a smart power meter fault assessment device, wherein the smart power meter fault assessment device has functions such as data processing, data communication and program running. The smart power meter fault assessment device can be a computer terminal device or other network device, and of course it can also be other devices with similar functions, and this embodiment does not limit this.

[0056] It should be noted that smart energy meters are important equipment in smart grids. With the development of smart grids, the number of smart energy meters installed has increased, and corresponding faults have also occurred frequently. Every year, a large number of smart energy meters fail in the grid and are removed for repair. The failure forms are diverse and complex and sudden. At present, for smart meters that are removed for repair, it is usually determined whether they need to be repaired after evaluation. However, the current fault assessment method does not take into account the faults of smart energy meters and the expiration of their service life, resulting in a one-sided and inaccurate fault assessment method.

[0057] In order to solve the above technical problems, in this embodiment, fault data information of the smart electric energy meter is obtained; an evaluation matrix is ​​constructed according to the fault data information; the index values ​​in the evaluation matrix are preprocessed to obtain a reference matrix corresponding to the evaluation matrix; the weights corresponding to each fault type are determined based on the reference matrix, and the reference matrix is ​​normalized and weighted based on the weights to obtain a target matrix; the evaluation index of the smart electric energy meter in different dimensions is determined according to the target matrix. In the above manner, the fault type of the smart electric energy meter is used as the evaluation indicator, and the fault of the smart electric energy meter is evaluated from different dimensions, so that the fault evaluation is more comprehensive and the accuracy of the evaluation is improved. Specifically, it can be implemented as follows.

[0058] In the specific implementation, in this embodiment, it is necessary to first obtain the fault data information of the smart energy meter, and the fault data information includes metering out-of-tolerance fault data, battery fault data, display fault data, communication fault data, and fee control fault data.

[0059] Step S20: constructing an evaluation matrix according to the fault data information.

[0060] After obtaining the above fault data information, in this embodiment, an evaluation matrix can be first constructed based on the evaluation value corresponding to each fault type. The constructed evaluation matrix is ​​as follows:

[0061]

[0062] Among them, i represents a number of faulty smart meters, j represents a number of fault types, V ij is the corresponding evaluation value, and the evaluation matrix is ​​V.

[0063] Step S30: preprocessing the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix.

[0064] After the evaluation matrix is ​​constructed, in this embodiment, each evaluation value in the evaluation matrix needs to be preprocessed, and the preprocessed evaluation values ​​can be used to construct a corresponding reference matrix.

[0065] The preprocessing process in this embodiment includes standardization and normalization.

[0066] When processing, it is necessary to first calculate the standard value based on the indicator value. The calculation process is as follows: , where x ij That is the calculated standard value, according to which the standardized matrix can be constructed. The standardized matrix is ​​as follows:

[0067]

[0068] Among them, Q is the constructed standardized matrix.

[0069] Furthermore, after obtaining the above-mentioned standardized matrix, the embodiment continues to perform normalization processing on the standardized matrix Q. Similarly, before performing normalization processing, calculations need to be performed based on the values ​​in the standardized matrix. The calculation process is: , then according to y ij Construct a reference matrix, for example:

[0070]

[0071] Among them, W is the constructed reference matrix.

[0072] Step S40: determining the weight corresponding to each fault type based on the reference matrix, and performing normalized weighted processing on the reference matrix based on the weight to obtain a target matrix.

[0073] According to the constructed reference matrix, the weight corresponding to each fault type can be calculated in this embodiment.

[0074] The specific calculation process is to first calculate the entropy value of each fault type based on the reference matrix, for example , e j It represents the entropy value of the jth fault type, and the corresponding weight value is , w j It represents the weight corresponding to the jth fault type. It should be noted that the weight corresponding to each fault type in this embodiment is

[0075] The sum is 1, that is, w1+w2+...+w j =1.

[0076] After obtaining the above weights, the present embodiment finally performs normalized weighting processing to obtain the final target matrix. It should be noted that each element z in the target matrix Z ij =w j *y ij , z ij Used to construct the target matrix Z.

[0077] Step S50: determining evaluation indexes of the smart electric energy meter in different dimensions according to the target matrix.

[0078] After obtaining the target matrix, the evaluation index of the smart power meter can be calculated based on the element values ​​in the target matrix. It should be noted that in this embodiment, the evaluation index of the smart power meter is calculated from different dimensions, specifically including the first dimension and the second dimension. The first dimension is based on the probability of occurrence of each fault type, and the second dimension is based on the correlation between each fault type.

[0079] This embodiment obtains fault data information of the smart power meter; constructs an evaluation matrix according to the fault data information; pre-processes the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; determines the weights corresponding to each fault type based on the reference matrix, and performs normalized weighted processing on the reference matrix based on the weight to obtain a target matrix; determines the evaluation index of the smart power meter in different dimensions according to the target matrix. In the above manner, the fault type of the smart power meter is used as the evaluation index, and the fault of the smart power meter is evaluated from different dimensions, so that the fault evaluation is more comprehensive and the accuracy of the evaluation is improved.

[0080] refer to Figure 2 , Figure 2 The figure is a flow chart of a second embodiment of a method for fault assessment of a smart electric energy meter according to the present invention.

[0081] Based on the first embodiment above, in the smart energy meter fault assessment method of this embodiment, step S50 further includes:

[0082] Step S501: determining a first ideal solution and a second ideal solution based on a preset ideal system.

[0083] It should be noted that, in this embodiment, the first ideal solution and the second ideal solution are determined by the comprehensive evaluation ideal solution method, wherein the first ideal solution represents the positive ideal solution, the second ideal solution represents the negative ideal solution, the preset ideal system is the positive ideal system and the negative ideal system, the first ideal solution is the maximum value in the positive ideal system, and the second ideal solution is the minimum value in the negative ideal system, for example, T1=( , ,..., ), the first ideal solution is the maximum value in T1.

[0084] Step S502: determining evaluation indexes of the smart electric energy meter in different dimensions according to the target matrix, the first ideal solution and the second ideal solution.

[0085] It should be noted that, in this embodiment, the evaluation index of the smart energy meter is determined from two different dimensions.

[0086] In some embodiments, it is assumed that the first ideal solution is max , the second ideal solution is min , we can get the first weighted distance, for example , the second weighted distance is such as , w j and z ij Referring to the above, the evaluation index under the first dimension is, for example, E1=d + / (d + +d - ).

[0087] In some embodiments, we continue to assume that the first ideal solution is max , the second ideal solution is min , we can get the first correlation coefficient, for example , the second correlation coefficient is , where represents a preset parameter, the value range is 0~1, and the specific value can be selected according to actual needs, which is not limited in this embodiment. The relationship between the correlation coefficient and the correlation degree is r=r i / i, where i is the number of fault types, r i is the above r1 or r2. Finally, the evaluation index under the second dimension can be calculated, E2=r + / (r + +r - ), r + Represents the first degree of association, r - Indicates the second degree of association.

[0088] In this embodiment, a first ideal solution and a second ideal solution are determined based on a preset ideal system, the first ideal solution corresponds to a solution in which an indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which an indicator reaches a worst value, a first weighted distance is calculated according to the target matrix and the first ideal solution; a second weighted distance is calculated according to the target matrix and the second ideal solution; an evaluation index of the smart electric energy meter in a first dimension is determined according to the first weighted distance and the second weighted distance, or a first correlation coefficient is calculated according to the target matrix, the first ideal solution and preset parameters; a second correlation coefficient is calculated according to the target matrix, the second ideal solution and preset parameters; a first correlation degree is determined based on the first correlation coefficient, and a second correlation degree is determined based on the second correlation coefficient; an evaluation index of the smart electric energy meter in a second dimension is determined according to the first correlation degree and the second correlation degree. The evaluation indexes of the smart electric energy meter in different dimensions are obtained respectively in the above manners, so that the fault assessment is more comprehensive and the accuracy of the fault assessment is improved.

[0089] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the intelligent electric energy meter fault assessment device of the present invention.

[0090] like Figure 3 As shown, the smart electric energy meter fault assessment device proposed in the embodiment of the present invention includes:

[0091] The acquisition module 10 is used to acquire fault data information of the smart electric energy meter.

[0092] The construction module 20 is used to construct an evaluation matrix according to the fault data information, and the evaluation matrix includes evaluation values ​​of each fault type.

[0093] The construction module 20 is used to preprocess the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix.

[0094] The construction module 20 is used to determine the weight corresponding to each fault type based on the reference matrix, and perform normalized weighted processing on the reference matrix based on the weight to obtain a target matrix.

[0095] The processing module 30 is used to determine the evaluation index of the smart electric energy meter in different dimensions according to the target matrix.

[0096] This embodiment obtains fault data information of the smart power meter; constructs an evaluation matrix according to the fault data information; pre-processes the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; determines the weights corresponding to each fault type based on the reference matrix, and performs normalized weighted processing on the reference matrix based on the weight to obtain a target matrix; determines the evaluation index of the smart power meter in different dimensions according to the target matrix. In the above manner, the fault type of the smart power meter is used as the evaluation index, and the fault of the smart power meter is evaluated from different dimensions, so that the fault evaluation is more comprehensive and the accuracy of the evaluation is improved.

[0097] In some embodiments, the construction module 20 is used to obtain the indicator values ​​in the evaluation matrix and calculate the standard values ​​based on the indicator values; construct a standardized matrix according to the standard values; and normalize the standardized matrix to obtain a reference matrix corresponding to the evaluation matrix.

[0098] In some embodiments, the construction module 20 is used to calculate the entropy value of each fault type based on the reference matrix; determine the weight corresponding to each fault type according to the entropy value of each fault type, wherein the sum of the weights corresponding to each fault type is 1; and perform normalized weighted processing on the reference matrix according to the weights corresponding to each fault type to obtain a target matrix.

[0099] In some embodiments, the processing module 30 is used to determine a first ideal solution and a second ideal solution based on a preset ideal system, wherein the first ideal solution corresponds to a solution in which an indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which an indicator reaches a worst value; and determine the evaluation index of the smart electric energy meter in different dimensions according to the target matrix and the first ideal solution and the second ideal solution.

[0100] In some embodiments, the processing module 30 is used to calculate a first weighted distance based on the target matrix and the first ideal solution; calculate a second weighted distance based on the target matrix and the second ideal solution; and determine an evaluation index of the smart power meter in a first dimension based on the first weighted distance and the second weighted distance.

[0101] In some embodiments, the processing module 30 is used to calculate a first correlation coefficient based on the target matrix, the first ideal solution and preset parameters; calculate a second correlation coefficient based on the target matrix, the second ideal solution and preset parameters; determine a first correlation degree based on the first correlation coefficient, and determine a second correlation degree based on the second correlation coefficient; determine an evaluation index of the smart power meter in the second dimension based on the first correlation degree and the second correlation degree.

[0102] An embodiment of the present application also provides a smart energy meter fault assessment device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the memory is used to store computer programs; the processor is used to implement the above-mentioned smart energy meter fault assessment method when executing the program stored in the memory.

[0103] The communication bus mentioned in the above-mentioned smart energy meter fault assessment device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0104] The communication interface is used for communication between the above-mentioned smart energy meter fault assessment device and other devices.

[0105] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0106] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0107] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0109] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0110] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0111] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0112] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0113] In addition, for technical details not fully described in this embodiment, reference may be made to the smart electric energy meter fault assessment method provided in any embodiment of the present invention, and will not be repeated here.

[0114] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0115] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0117] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0118] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

Claims

1. A method for evaluating faults of a smart electric energy meter, characterized in that: The smart electric energy meter fault assessment method comprises: Obtain fault data information of smart electric energy meters; Constructing an evaluation matrix according to the fault data information, wherein the evaluation matrix includes evaluation values ​​of each fault type; Preprocessing the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; Determine the weight corresponding to each fault type based on the reference matrix, and perform normalized weighted processing on the reference matrix based on the weight to obtain a target matrix; Determine the evaluation index of the smart electric energy meter in different dimensions according to the target matrix; Wherein, determining the evaluation index of the smart electric energy meter in different dimensions according to the target matrix further includes: Determine a first ideal solution and a second ideal solution based on a preset ideal system, wherein the first ideal solution corresponds to a solution in which an indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which an indicator reaches a worst value, the first ideal solution is a maximum value in a positive ideal system, and the second ideal solution is a minimum value in a negative ideal system; A first weighted distance is calculated based on the target matrix and the first ideal solution. The first weighted distance , the first ideal solution is , z ij are the elements in the target matrix Z, w j is the weight corresponding to the jth fault type; A second weighted distance is calculated based on the target matrix and the second ideal solution, and the second weighted distance , the second ideal solution is , z ij are the elements in the target matrix Z, w j is the weight corresponding to the jth fault type; The evaluation index of the smart electric energy meter in the first dimension is determined according to the first weighted distance and the second weighted distance. The evaluation index in the first dimension is E1=d + / (d + +d - ); and, calculating a first correlation coefficient according to the target matrix, the first ideal solution and preset parameters, the first correlation coefficient , the first ideal solution is , z ij are the elements in the target matrix Z, It is a preset parameter, and its value range is 0~1; The second correlation coefficient is calculated according to the target matrix, the second ideal solution and the preset parameters. , the second ideal solution is , z ij are the elements in the target matrix Z, It is a preset parameter, and its value range is 0~1; determining a first degree of association based on the first correlation coefficient, and determining a second degree of association based on the second correlation coefficient; The evaluation index of the smart electric energy meter in the second dimension is determined according to the first correlation degree and the second correlation degree, and the evaluation index E2 in the second dimension is r + / (r + +r - ), where r + is the first correlation, r - is the second correlation, r + =r1 / J, r - =r2 / J, J is the number of fault types.

2. The smart energy meter fault assessment method according to claim 1, characterized in that: The preprocessing includes standardization and normalization. The preprocessing of the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix includes: Obtaining the index values ​​in the evaluation matrix, and calculating the standard values ​​based on the index values; constructing a standardized matrix according to the standard values; The standardized matrix is ​​normalized to obtain a reference matrix corresponding to the evaluation matrix.

3. The smart energy meter fault assessment method according to claim 1, characterized in that: The step of determining the weight corresponding to each fault type based on the reference matrix, and performing normalized weighted processing on the reference matrix based on the weight to obtain a target matrix includes: Calculate the entropy value of each fault type based on the reference matrix; Determine a weight corresponding to each fault type according to the entropy value of each fault type, wherein the sum of the weights corresponding to each fault type is 1; The reference matrix is ​​normalized and weighted according to the weights corresponding to the fault types to obtain a target matrix.

4. A smart electric energy meter fault assessment device, characterized in that: The intelligent electric energy meter fault assessment device comprises: An acquisition module, used to acquire fault data information of a smart electric energy meter; A construction module, used to construct an evaluation matrix according to the fault data information, wherein the evaluation matrix includes evaluation values ​​of each fault type; The construction module is used to preprocess the index values ​​in the evaluation matrix to obtain a reference matrix corresponding to the evaluation matrix; The construction module is used to determine the weight corresponding to each fault type based on the reference matrix, and perform normalized weighted processing on the reference matrix based on the weight to obtain a target matrix; A processing module, used to determine the evaluation index of the smart electric energy meter in different dimensions according to the target matrix; The processing module is further used to determine a first ideal solution and a second ideal solution based on a preset ideal system, wherein the first ideal solution corresponds to a solution in which the indicator reaches an optimal value, and the second ideal solution corresponds to a solution in which the indicator reaches a worst value, and the first ideal solution is a maximum value in the positive ideal system, and the second ideal solution is a minimum value in the negative ideal system; A first weighted distance is calculated based on the target matrix and the first ideal solution. The first weighted distance , the first ideal solution is , z ij are the elements in the target matrix Z, w j is the weight corresponding to the jth fault type; A second weighted distance is calculated based on the target matrix and the second ideal solution, and the second weighted distance , the second ideal solution is , z ij are the elements in the target matrix Z, w j is the weight corresponding to the jth fault type; The evaluation index of the smart electric energy meter in the first dimension is determined according to the first weighted distance and the second weighted distance. The evaluation index in the first dimension is E1=d + / (d + +d - ); and, calculating a first correlation coefficient according to the target matrix, the first ideal solution and preset parameters, the first correlation coefficient , the first ideal solution is , z ij are the elements in the target matrix Z, It is a preset parameter, and its value range is 0~1; The second correlation coefficient is calculated according to the target matrix, the second ideal solution and the preset parameters. , the second ideal solution is , z ij are the elements in the target matrix Z, It is a preset parameter, and its value range is 0~1; determining a first degree of association based on the first correlation coefficient, and determining a second degree of association based on the second correlation coefficient; The evaluation index of the smart electric energy meter in the second dimension is determined according to the first correlation degree and the second correlation degree, and the evaluation index E2 in the second dimension is r + / (r + +r - ), where r + is the first correlation, r - is the second correlation, r + =r1 / J, r - =r2 / J, J is the number of fault types.

5. The intelligent electric energy meter fault assessment device according to claim 4, characterized in that: The construction module is used to obtain the index values ​​in the evaluation matrix and calculate the standard values ​​based on the index values; constructing a standardized matrix according to the standard values; The standardized matrix is ​​normalized to obtain a reference matrix corresponding to the evaluation matrix.

6. The intelligent electric energy meter fault assessment device according to claim 4, characterized in that: The building module is used to calculate the entropy value of each fault type based on the reference matrix; Determine a weight corresponding to each fault type according to the entropy value of each fault type, wherein the sum of the weights corresponding to each fault type is 1; The reference matrix is ​​normalized and weighted according to the weights corresponding to the fault types to obtain a target matrix.

Images