Big data-based electric energy metering management system and method

Abstract

The present application relates to the technical field of electric energy metering management, and particularly relates to an electric energy metering management system and method based on big data, which comprises the following steps: analyzing the wiring degradation degree of the electric meter based on the building wall state data and the wiring state data of the electric meter; analyzing the metering performance abnormality degree of the electric meter based on the metering parameter data and the wiring state data of the electric meter; evaluating the wiring looseness risk of the electric meter according to the wiring degradation degree analysis result and the metering performance abnormality degree analysis result; and warning the maintenance personnel to repair the electric meter wiring according to the wiring looseness risk evaluation result, so that the electric energy metering management efficiency is improved and the electric energy metering management cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of electricity metering management technology, and in particular to an electricity metering management system and method based on big data. Background Technology

[0002] With the development of intelligent power systems and the energy internet, electricity metering management is gradually shifting from traditional mechanical metering to digital and networked methods, becoming a core link in ensuring electricity market transactions and optimizing the economic operation of the power grid. However, in the process of iterative updates to electricity metering technology, its management still faces many challenges. On the one hand, the widespread integration of new power electronic devices and distributed energy resources leads to a surge in harmonic content in the power grid and complex load characteristics, making it difficult for traditional steady-state frequency-based electricity metering models to accurately obtain dynamic electricity information. On the other hand, the physical reliability of electricity metering devices throughout their lifecycle is becoming increasingly prominent. For example, issues such as loose terminals and deteriorated contact resistance persist, directly affecting the accuracy and stability of electricity metering data. Therefore, when analyzing electricity metering errors, it is necessary not only to consider the performance drift of internal components of the meter but also to conduct in-depth analysis of systemic factors such as the external environment, installation process, and operation and maintenance level. Especially in densely populated urban areas, when large transportation vehicles such as subways frequently pass by buildings where electricity meters are installed, the low-frequency vibrations generated by their operation can resonate with the building structure, significantly amplifying the vibration amplitude of the walls. This continuous mechanical impact not only accelerates the loosening process of the terminal screws, but also exacerbates the oxidation and micro-deformation of the contact surface, becoming an important cause of contact resistance degradation.

[0003] However, existing technologies for electricity metering management primarily focus on optimizing metering parameters such as harmonic compensation, temperature drift correction, and load adaptive range switching. For example, most existing electricity metering error analysis models assume that the wiring is always reliable, neglecting to consider the time-varying characteristics of contact resistance caused by loose wiring. Furthermore, existing technologies lack systematic monitoring and analysis of the mechanical vibration transmission path of the meter mounting base caused by special operating conditions such as building resonance due to subway operation and its long-term impact on the stability of the terminal connections. This makes it difficult to promptly identify and locate electricity metering inaccuracies caused by wiring deterioration in actual operation and maintenance, often requiring manual on-site inspections, resulting in low efficiency and high costs in electricity metering management.

[0004] To address these issues, this application presents a big data-based electricity metering management system and method. Summary of the Invention

[0005] The purpose of this invention is to provide a big data-based electricity metering management system and method, which analyzes the degree of wiring deterioration and the degree of metering performance abnormality of electricity meters; thereby realizing the assessment of the risk of loose wiring of electricity meters; thus improving the efficiency of electricity metering management and reducing the cost of electricity metering management.

[0006] This invention is implemented as follows:

[0007] In a first aspect, the present invention provides a method for electricity metering management based on big data, comprising the following steps:

[0008] S1. Obtain the metering parameter data and wiring status data of the electricity meter, and at the same time obtain the building wall status data of the electricity meter installation location;

[0009] S2. Analyze the degree of wiring deterioration of the electricity meter based on the building wall condition data and the wiring condition data of the electricity meter installation location.

[0010] S3. Analyze the degree of abnormality in the metering performance of the electricity meter based on the metering parameter data and wiring status data;

[0011] S4. Based on the analysis results of the degree of deterioration of the meter wiring and the degree of abnormality in metering performance, assess the risk of loose meter wiring;

[0012] S5. Based on the risk assessment results of loose wiring in the electricity meter, provide early warnings to maintenance personnel regarding meter wiring repairs.

[0013] Based on the above scheme, in step S2, the degree of wiring degradation of the electricity meter is analyzed based on the building wall condition data and the wiring condition data of the electricity meter installation location. Specifically, this includes:

[0014] S21. Extract the building wall status data and the meter wiring status data at the meter installation location;

[0015] S22. Based on the building wall condition data and the meter wiring condition data at the meter installation location, analyze the degree of wiring degradation of the meter and obtain the analysis results of the degree of wiring degradation of the meter.

[0016] Based on the above scheme, the preferred method is to analyze the degree of wiring degradation of the electricity meter in step S22, specifically including:

[0017] S221. Based on the building wall status data and the wiring status data of the meter installation location, analyze the vibration torque attenuation degree of the wiring to obtain the analysis results of the vibration torque attenuation degree of the wiring.

[0018] S222. Based on the building wall condition data and the wiring condition data of the meter installation location, analyze the environmental corrosion degree of the wiring to obtain the analysis results of the environmental corrosion degree of the wiring.

[0019] S223. Based on the analysis results of the vibration torque attenuation degree and the environmental corrosion degree of the wiring, establish a vibration environment coupling effect analysis model, calculate the vibration environment coupling effect index, and conduct coupling analysis on the torque attenuation caused by vibration and the corrosion of wiring materials caused by the environment to obtain the vibration environment coupling analysis results.

[0020] S224. Based on the analysis results of the vibration torque attenuation degree of the wiring, the analysis results of the environmental corrosion degree, and the vibration environment coupling analysis results, the degree of wiring degradation of the meter is analyzed, and the analysis results of the degree of wiring degradation of the meter are obtained.

[0021] Based on the above scheme, the preferred method is to analyze the degree of abnormality in the metering performance of the meter in step S3, based on the metering parameter data and wiring status data of the meter. This specifically includes the following steps:

[0022] S31. Extract the metering parameter data and wiring status data of the electricity meter;

[0023] S32. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of abnormality in the metering performance and obtain the analysis results of the degree of abnormality in the metering performance.

[0024] Based on the above scheme, the preferred method is to analyze the degree of abnormality in the metering performance of the electricity meter in step S32, which includes the following specific steps:

[0025] S321. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of harmonic coupling anomaly of the electricity meter and obtain the analysis results of the degree of harmonic coupling anomaly of the electricity meter.

[0026] S322. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of error coupling anomaly of the electricity meter and obtain the analysis results of the degree of error coupling anomaly of the electricity meter.

[0027] S323. Based on the analysis results of the degree of harmonic coupling anomaly and the degree of error coupling anomaly of the meter, the degree of metering performance anomaly of the meter is analyzed, and the analysis results of the degree of metering performance anomaly of the meter are obtained.

[0028] Based on the above scheme, in step S4, the risk of loose wiring of the electricity meter is assessed according to the analysis results of the degree of wiring deterioration and the degree of metering performance abnormality. This includes the following specific steps:

[0029] S41. Obtain the analysis results of the wiring deterioration degree of the electricity meter obtained from the analysis, and at the same time obtain the analysis results of the abnormality degree of the metering performance of the electricity meter obtained from the analysis.

[0030] S42. Based on the analysis results of the degree of deterioration of the meter wiring and the degree of abnormality in metering performance, the risk of loose wiring of the meter is assessed, and the assessment result of the risk of loose wiring of the meter is obtained.

[0031] Based on the above scheme, in step S5, according to the risk assessment results of loose meter wiring, an early warning for meter wiring repair is issued to maintenance personnel, specifically including:

[0032] S51. Obtain the assessment results of the risk of loose wiring of the electricity meter obtained from the assessment.

[0033] S52. Preset a loose wiring risk threshold. When the risk assessment result of loose wiring of the meter is greater than the loose wiring risk threshold, an early warning will be issued to the maintenance personnel for meter wiring repair.

[0034] Secondly, the present invention provides a big data-based electricity metering management system, comprising:

[0035] The data acquisition module is used to acquire the metering parameter data and wiring status data of the electricity meter, as well as the building wall status data of the electricity meter installation location;

[0036] The wiring degradation analysis module is used to analyze the degree of wiring degradation of the electricity meter based on the building wall condition data of the meter installation location and the wiring condition data of the electricity meter.

[0037] The metering performance anomaly analysis module is used to analyze the degree of anomaly in the metering performance of the electricity meter based on the metering parameter data and wiring status data.

[0038] The loose wiring risk assessment module is used to assess the risk of loose wiring in the electricity meter based on the analysis results of the degree of wiring deterioration and the degree of metering performance abnormality.

[0039] The electricity meter wiring maintenance early warning module is used to provide early warnings to maintenance personnel regarding electricity meter wiring maintenance based on the risk assessment results of loose electricity meter wiring.

[0040] The control module is used to control the operation of the data acquisition module, the wiring deterioration analysis module, the metering performance anomaly analysis module, the wiring looseness risk assessment module, and the meter wiring repair early warning module.

[0041] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a big data-based electricity metering management method by calling the computer program stored in the memory.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] This invention analyzes the degree of wiring degradation of electricity meters based on building wall condition data at the meter installation location and wiring condition data; it analyzes the degree of metering performance abnormality based on metering parameter data and wiring condition data; it assesses the risk of loose meter wiring based on the analysis results of wiring degradation and metering performance abnormality; and it provides early warnings for meter wiring repair to maintenance personnel based on the assessment results of loose meter wiring risk, thereby improving the efficiency of electricity metering management and reducing electricity metering management costs. Attached Figure Description

[0044] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is a schematic diagram of the overall process of the big data-based electricity metering management method of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of the big data-based electricity metering management system of the present invention;

[0047] Figure 3 This is a flowchart illustrating the steps of the big data-based electricity metering management method of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0049] Example 1

[0050] like Figure 1 , Figure 3 As shown, this embodiment provides a big data-based electricity metering management method, which specifically includes the following steps:

[0051] S1. Obtain the metering parameter data and wiring status data of the electricity meter, and at the same time obtain the building wall status data of the electricity meter installation location;

[0052] S2. Analyze the degree of wiring deterioration of the electricity meter based on the building wall condition data and the wiring condition data of the electricity meter installation location.

[0053] S3. Analyze the degree of abnormality in the metering performance of the electricity meter based on the metering parameter data and wiring status data;

[0054] S4. Based on the analysis results of the degree of deterioration of the meter wiring and the degree of abnormality in metering performance, assess the risk of loose meter wiring;

[0055] S5. Based on the risk assessment results of loose wiring in the electricity meter, provide early warnings to maintenance personnel regarding meter wiring repairs.

[0056] As a preferred technical solution of the present invention, step S2 analyzes the degree of wiring degradation of the electricity meter based on the building wall condition data of the meter installation location and the wiring condition data of the electricity meter, specifically including:

[0057] S21. Extract the building wall status data and the meter wiring status data at the meter installation location;

[0058] S22. Based on the building wall condition data and the meter wiring condition data at the meter installation location, analyze the degree of wiring degradation of the meter and obtain the analysis results of the degree of wiring degradation of the meter.

[0059] As a preferred technical solution of the present invention, step S22 involves analyzing the degree of degradation of the meter's wiring, specifically including:

[0060] S221. Based on the building wall condition data and meter wiring condition data at the meter installation location, the vibration torque attenuation degree of the wiring is analyzed to obtain the analysis results. In buildings frequently traversed by large transportation vehicles such as subways, the low-frequency vibrations generated by the operation of these vehicles resonate with the building structure, amplifying the wall vibration amplitude at the meter installation location and further accelerating the loosening process of the terminal screws. Therefore, this step quantifies the attenuation effect on the pre-tightening force of the wiring screws by integrating the accumulated energy input under vibration. The attenuation amplitude of the pre-tightening force of the wiring screws caused by wall vibration is quantified by the natural logarithm function, thereby quantifying the vibration torque attenuation degree of the wiring. The calculation formula for the vibration torque attenuation degree of the wiring is: Where Zn is the degree of vibration torque attenuation of the wiring, Ln is the torque of the wiring screw in the wiring status data, Lo is the initial preload of the wiring screw in the wiring status data, K is the damping coefficient of the wiring material in the wiring status data, t1 and t2 are the start and end times of the wall vibration time interval at the meter installation location in the building wall status data, and a(t) is the wall vibration acceleration at the meter installation location at time t in the building wall status data.

[0061] S222. Based on the building wall condition data and meter wiring condition data at the meter installation location, the environmental corrosion degree of the wiring is analyzed to obtain the analysis results. This step quantifies the cumulative effect of oxidation corrosion of the wiring material under temperature and its interaction with changes in contact resistance through an integral term, thereby realizing the analysis of the environmental corrosion degree of the wiring. The formula for calculating the environmental corrosion degree of the wiring is: Where Hf represents the degree of environmental corrosion of the wiring, Ro represents the initial contact resistance of the meter wiring in the wiring status data, Ea represents the oxidation activation energy of the meter wiring material in the wiring status data, R represents the gas constant, and T(t) represents the temperature of the meter installation location at time t in the building wall status data. The temperature gradient at the location of the electricity meter in the building wall condition data;

[0062] S223. Based on the analysis results of the vibration torque attenuation degree and environmental corrosion degree of the wiring, a vibration-environment coupling effect analysis model is established, and the vibration-environment coupling effect index is calculated. A coupling analysis is conducted on the torque attenuation caused by vibration and the corrosion of the wiring material caused by the environment, yielding the vibration-environment coupling analysis results. In this embodiment, the vibration of the building wall can cause the wiring to loosen and the wiring gap to increase, thus accelerating the oxidation and corrosion rate of the wiring in the environment. Simultaneously, when the wiring experiences cross-sectional corrosion under environmental corrosion, the rigidity of the wiring material decreases, thereby weakening the vibration resistance of the wiring. Therefore, this step, based on the Jahn-Teller effect theory, establishes a two-way feedback model of vibration and corrosion, using weighting coefficients and derivative terms to characterize the dynamic coupling strength between the vibration torque attenuation degree and the environmental corrosion degree. The formula for calculating the vibration-environment coupling effect index is: Where Ce is the vibration-environment coupling effect index, These represent the decay rate of vibration torque attenuation over time and the catalytic rate of environmental corrosion attenuation over temperature, respectively. As a weighting factor;

[0063] S224. Based on the analysis results of the vibration torque attenuation degree of the wiring, the analysis results of the environmental corrosion degree, and the vibration-environment coupling analysis results, the degree of wiring degradation of the meter is analyzed to obtain the analysis results of the wiring degradation degree of the meter. This step uses a fuzzy adaptive weight regulator to dynamically adjust the weights to adaptively optimize the weight allocation corresponding to the vibration torque attenuation degree, environmental corrosion degree, and vibration-environment coupling effect index of the wiring, thereby achieving the purpose of accurately quantifying the degree of wiring degradation. At the same time, this embodiment uses a fuzzy adaptive weight regulator to dynamically adjust the weights, which can also accurately reflect whether the wiring degradation during the use of the meter is dominated by vibration torque attenuation, environmental corrosion, or the coupling effect of both. The formula for calculating the degree of wiring degradation is: Where LH represents the degree of wiring deterioration of the electricity meter. The weights for the effects of vibration torque attenuation, environmental corrosion, and coupling effects are respectively:

[0064] It should be noted that the above weighting factors and influence weights are all dynamically adjusted using a fuzzy adaptive weight adjuster; the specific adjustment process is as follows:

[0065] Calculate vibration intensity factor Where vmax is the critical acceleration that leads to fatigue failure of the wiring material;

[0066] Calculate corrosion rate factor ,in, The corrosion rate represents the degree of environmental corrosion over time, and Fs is the threshold at which the corrosion rate of the wiring material reaches the point where immediate maintenance is required.

[0067] Calculate the coupling strength factor of the vibration environment Where max() is the function to find the maximum value;

[0068] Using the calculated vibration intensity factor, corrosion rate factor, and vibration environment coupling strength factor as inputs to the fuzzy adaptive weight regulator directly reflects the real-time intensity of vibration impact on the meter wiring, thus reflecting the instantaneous change in vibration intensity and improving the sensitivity of the weights to the instantaneous changes in the result after vibration torque attenuation. Simultaneously, the corrosion rate factor is directly related to the real-time development of the wiring corrosion process. If the environmental corrosion degree of S222 is used as the input to the fuzzy adaptive weight regulator, it only reflects the cumulative corrosion effect and lacks sensitivity to sudden corrosion events (such as a sudden increase in corrosive gas concentration). Therefore, this embodiment uses the corrosion rate factor as the input to the fuzzy adaptive weight regulator, thereby improving the sensitivity of the wiring degradation calculation formula to sudden corrosion events.

[0069] Using a fuzzy adaptive weight adjuster as well as Make dynamic adjustments;

[0070] For example, when vibrations from large transportation vehicles such as subways are detected, causing increased resonance or a sudden increase in corrosion rate in building walls, thus increasing the vibration intensity factor or corrosion rate factor and exacerbating the degree of vibration torque attenuation or environmental corrosion, a fuzzy adaptive weight adjuster automatically reduces the intensity factor. To avoid the vibration environment coupling effect index being dominated by a single factor, when it is found that the coupling effect between the torque attenuation caused by vibration and the corrosion of the wiring material caused by the environment is aggravated, proactive measures are taken to improve the coupling effect. This strengthens the contribution of the coupling effect to the degree of wiring degradation, thus increasing the coupling effect exponent of the vibration environment.

[0071] As a preferred technical solution of the present invention, step S3 analyzes the degree of abnormality in the metering performance of the meter based on the metering parameter data and wiring status data of the meter, specifically including the following steps:

[0072] S31. Extract the metering parameter data and wiring status data of the electricity meter;

[0073] S32. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of abnormality in the metering performance and obtain the analysis results of the degree of abnormality in the metering performance.

[0074] As a preferred technical solution of the present invention, step S32, which analyzes the degree of abnormality in the metering performance of the electricity meter, includes the following specific steps:

[0075] S321. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of harmonic coupling anomaly of the electricity meter to obtain the analysis result of the degree of harmonic coupling anomaly of the electricity meter. This step quantifies the degree of harmonic coupling anomaly of the electricity meter by comparing the measured maximum total harmonic distortion rate with the specified total harmonic distortion rate limit. The calculation formula for the degree of harmonic coupling anomaly of the electricity meter is as follows: Where Hd is the degree of harmonic coupling abnormality of the meter, XBs is the total harmonic distortion rate limit in the metering parameter data, XBmax is the maximum total harmonic distortion rate of the meter measured during the use of the currently installed wiring in the metering parameter data, Time is the usage time of the currently installed wiring in the wiring status data, and txb is the total duration of harmonics occurring during the use of the currently installed wiring in the metering parameter data.

[0076] S322. Based on the metering parameter data and wiring status data of the electricity meter, the degree of error coupling anomaly is analyzed to obtain the analysis results. This step considers the ratio of the absolute error of the meter's active power to the rated active power, which directly reflects the accuracy of the metering. Simultaneously, the ratio of the current wiring usage time to the total meter usage time is introduced to further assess the long-term impact of the wiring status on the meter's error. The formula for calculating the degree of error coupling anomaly of the electricity meter is: Where Wc represents the degree of error coupling anomaly in the meter, and P represents the rated active power of the meter in the metering parameter data. DT represents the absolute error of the active power of the meter measured during the current installation wiring period in the metering parameter data; Time represents the usage time of the current installation wiring in the wiring status data; and DT represents the total usage time of the meter in the wiring status data.

[0077] S323. Based on the analysis results of the harmonic coupling anomaly degree and the error coupling anomaly degree of the electricity meter, the metering performance anomaly degree is analyzed to obtain the metering performance anomaly degree analysis result. This step takes into account that the impact of the harmonic coupling anomaly degree and the error coupling anomaly degree of the electricity meter on the metering performance anomaly degree varies in different scenarios. Therefore, this embodiment introduces a weighting method to dynamically adjust the influence of the two. The calculation formula for the metering performance anomaly degree of the electricity meter is: Where JL represents the degree of abnormality in the metering performance of the electricity meter, and b1 and b2 represent the weights of the abnormal harmonic coupling and the abnormal error coupling, respectively.

[0078] As a preferred technical solution of the present invention, step S4 assesses the risk of loose wiring of the electricity meter based on the analysis results of the degree of wiring deterioration and the analysis results of the degree of metering performance abnormality, including the following specific steps:

[0079] S41. Obtain the analysis results of the wiring deterioration degree of the electricity meter obtained from the analysis, and at the same time obtain the analysis results of the abnormality degree of the metering performance of the electricity meter obtained from the analysis.

[0080] S42. Based on the analysis results of the degree of wiring deterioration and the degree of metering performance abnormality, the risk of loose wiring of the electricity meter is assessed, and the assessment result of the risk of loose wiring of the electricity meter is obtained. This step takes into account that the degree of wiring deterioration and the degree of metering performance abnormality of the electricity meter have different impacts on the risk of loose wiring under different scenarios and different metering requirements. Therefore, this embodiment introduces a weighting method to dynamically adjust the influence of the two. The assessment formula for the risk of loose wiring of the electricity meter is: Wherein, SD represents the risk of loose wiring in the electricity meter, and c1 and c2 represent the weights of the impact of wiring deterioration and the impact of abnormal metering performance, respectively.

[0081] As a preferred technical solution of the present invention, step S5 provides an early warning for meter wiring repair to maintenance personnel based on the risk assessment results of loose meter wiring, specifically including:

[0082] S51. Obtain the assessment results of the risk of loose wiring of the electricity meter obtained from the assessment;

[0083] S52. A preset loose wiring risk threshold is established. When the risk assessment result of loose wiring of the electricity meter exceeds the loose wiring risk threshold, an early warning for meter wiring repair is issued to the maintenance personnel. The parameters (e.g., weights and thresholds) in this embodiment are obtained experimentally by those skilled in the art. The specific experimental method is as follows: Metering parameter data and wiring status data of multiple historical electricity meters are obtained, along with building wall status data for the corresponding meter installation location; the metering parameter data, wiring status data, and corresponding building wall status data are substituted into the steps of this embodiment to obtain the risk assessment results of loose wiring of multiple historical electricity meters; the judgment results of whether loose wiring exists in multiple historical electricity meters are obtained; the risk assessment results of loose wiring of multiple historical electricity meters and the judgment results of whether loose wiring exists in the corresponding electricity meters are imported into the fitting software, and the values ​​of the preset parameters (e.g., weights and thresholds) that meet the highest accuracy rate for judging loose wiring risk are output.

[0084] Example 2

[0085] like Figure 2 As shown, this embodiment provides a big data-based electricity metering management system, including:

[0086] The data acquisition module is used to acquire the metering parameter data and wiring status data of the electricity meter, as well as the building wall status data of the electricity meter installation location;

[0087] The wiring degradation analysis module is used to analyze the degree of wiring degradation of the electricity meter based on the building wall condition data of the meter installation location and the wiring condition data of the electricity meter.

[0088] The metering performance anomaly analysis module is used to analyze the degree of anomaly in the metering performance of the electricity meter based on the metering parameter data and wiring status data.

[0089] The loose wiring risk assessment module is used to assess the risk of loose wiring in the electricity meter based on the analysis results of the degree of wiring deterioration and the degree of metering performance abnormality.

[0090] The electricity meter wiring maintenance early warning module is used to provide early warnings to maintenance personnel regarding electricity meter wiring maintenance based on the risk assessment results of loose electricity meter wiring.

[0091] The control module is used to control the operation of the data acquisition module, the wiring deterioration analysis module, the metering performance anomaly analysis module, the wiring looseness risk assessment module, and the meter wiring repair early warning module.

[0092] The steps for implementing the corresponding functions of each parameter and each unit module in the big data-based electricity metering management system of the present invention can be referred to the parameters and steps in the embodiments of the big data-based electricity metering management method above, and will not be repeated here.

[0093] Example 3

[0094] An electronic device according to an embodiment of the present invention includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a big data-based electricity metering management method by calling the computer program stored in the memory. It should be noted that all computer programs for the big data-based electricity metering management method are implemented using C language. The data acquisition module, wiring deterioration analysis module, metering performance anomaly analysis module, wiring looseness risk assessment module, meter wiring repair early warning module, and control module are all controlled by a remote server.

[0095] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0096] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A big data-based electricity metering management method, characterized in that, Includes the following steps: S1. Obtain the metering parameter data and wiring status data of the electricity meter, and at the same time obtain the building wall status data of the electricity meter installation location; S2. Based on the building wall status data of the electricity meter installation location and the wiring status data of the electricity meter, analyze the degree of wiring degradation of the electricity meter. The analysis of the degree of wiring degradation of the electricity meter specifically includes: S221. Based on the building wall condition data and meter wiring condition data at the meter installation location, analyze the vibration torque attenuation of the wiring to obtain the analysis results of the vibration torque attenuation of the wiring; the calculation formula for the vibration torque attenuation of the wiring is: Where Zn is the degree of vibration torque attenuation of the wiring, Ln is the torque of the wiring screw in the wiring status data, Lo is the initial preload of the wiring screw in the wiring status data, K is the damping coefficient of the wiring material in the wiring status data, t1 and t2 are the start and end times of the wall vibration time interval at the meter installation location in the building wall status data, and a(t) is the wall vibration acceleration at the meter installation location at time t in the building wall status data. S222. Based on the building wall condition data and meter wiring condition data at the meter installation location, the environmental corrosion level of the wiring is analyzed to obtain the analysis results. The formula for calculating the environmental corrosion level of the wiring is: Where Hf represents the degree of environmental corrosion of the wiring, Ro represents the initial contact resistance of the meter wiring in the wiring status data, Ea represents the oxidation activation energy of the meter wiring material in the wiring status data, R represents the gas constant, and T(t) represents the temperature of the meter installation location at time t in the building wall status data. The temperature gradient at the location of the electricity meter in the building wall condition data; S223. Based on the analysis results of the vibration torque attenuation degree and environmental corrosion degree of the wiring, a vibration-environment coupling effect analysis model is established, and the vibration-environment coupling effect index is calculated. A coupling analysis is conducted on the torque attenuation caused by vibration and the corrosion of the wiring material caused by the environment, yielding the vibration-environment coupling analysis results. The formula for calculating the vibration-environment coupling effect index is: Where Ce is the vibration-environment coupling effect index, These represent the decay rate of vibration torque attenuation over time and the catalytic rate of environmental corrosion attenuation over temperature, respectively. As a weighting factor; S224. Based on the analysis results of the vibration torque attenuation degree of the wiring, the analysis results of the environmental corrosion degree, and the vibration environment coupling analysis results, the degree of wiring degradation of the meter is analyzed, and the analysis results of the degree of wiring degradation of the meter are obtained. S3. Analyze the degree of abnormality in the metering performance of the electricity meter based on the metering parameter data and wiring status data; S4. Based on the analysis results of the degree of deterioration of the meter wiring and the degree of abnormality in metering performance, assess the risk of loose meter wiring; S5. Based on the risk assessment results of loose wiring in the electricity meter, provide early warnings to maintenance personnel regarding meter wiring repairs.

2. The big data-based electricity metering management method according to claim 1, characterized in that, The analysis of the degree of abnormality in the metering performance of the electricity meter includes the following specific steps: S321. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of harmonic coupling anomaly of the electricity meter and obtain the analysis results of the degree of harmonic coupling anomaly of the electricity meter. S322. Based on the metering parameter data and wiring status data of the electricity meter, analyze the degree of error coupling anomaly of the electricity meter and obtain the analysis results of the degree of error coupling anomaly of the electricity meter. S323. Based on the analysis results of the degree of harmonic coupling anomaly and the degree of error coupling anomaly of the meter, the degree of metering performance anomaly of the meter is analyzed, and the analysis results of the degree of metering performance anomaly of the meter are obtained.

3. The big data-based electricity metering management method according to claim 2, characterized in that, In step S5, based on the risk assessment results of loose meter wiring, an early warning for meter wiring repair is issued to maintenance personnel, specifically including: S51. Obtain the assessment results of the risk of loose wiring of the electricity meter obtained from the assessment; S52. Preset a loose wiring risk threshold. When the risk assessment result of loose wiring of the meter is greater than the loose wiring risk threshold, an early warning will be issued to the maintenance personnel for meter wiring repair.

4. A big data-based electricity metering management system, used to implement the big data-based electricity metering management method according to any one of claims 1-3, characterized in that, The system includes: The data acquisition module is used to acquire metering parameter data and wiring status data of the electricity meter, as well as the building wall status data of the electricity meter installation location; the wiring deterioration analysis module is used to analyze the degree of wiring deterioration of the electricity meter based on the building wall status data of the electricity meter installation location and the electricity meter wiring status data. The metering performance anomaly analysis module is used to analyze the degree of anomaly in the metering performance of the electricity meter based on the metering parameter data and wiring status data. The loose wiring risk assessment module is used to assess the risk of loose wiring in the electricity meter based on the analysis results of the degree of wiring deterioration and the degree of metering performance abnormality. The electricity meter wiring maintenance early warning module is used to provide early warnings to maintenance personnel regarding electricity meter wiring maintenance based on the risk assessment results of loose electricity meter wiring. The control module is used to control the operation of the data acquisition module, the wiring deterioration analysis module, the metering performance anomaly analysis module, the wiring looseness risk assessment module, and the meter wiring repair early warning module.

5. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the big data-based electricity metering management method as described in any one of claims 1-3 by calling the computer program stored in the memory.

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