An electricity and electricity charge risk internal control supervision and management platform and a control method thereof

Abstract

The application discloses a power and electricity risk internal control supervision and management platform and a control method thereof, comprising a power collection module, which is used for collecting power consumption information of a user end; an electricity calculation module, which is used for classifying users and then calculating electricity of the users according to electricity calculation rules corresponding to each type of user; a meter monitoring module, which is used for monitoring metering data of a meter of the user end; a risk assessment module, which is used for assessing risks of the user end according to data output by the electricity calculation module and the meter monitoring module; and a risk management module, which is used for retrieving corresponding management processes from a database according to an assessment result of the risk assessment module and outputting the management processes. The application can improve the defects of the prior art, can accurately predict risk users in a power grid, and can reduce electricity arrears of the risk users while guaranteeing use experience of most high-quality customers.

Description

Technical Field

[0001] This invention relates to the field of electricity marketing business technology, and in particular to an internal control and supervision platform for electricity and electricity fee risks and its control method. Background Technology

[0002] As electricity consumption increases, the risk of power companies not collecting electricity bills also increases. Finding a balance between providing high-quality electricity services to customers and ensuring the normal collection of electricity bills by power companies is one of the hot research topics in this field. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an internal control and supervision platform for electricity and electricity bill risks and its control method, which can overcome the shortcomings of the prior art, accurately predict risky users in the power grid, and reduce the problem of electricity bill arrears for risky users while ensuring the user experience of most high-quality customers.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0005] A power consumption and electricity cost risk internal control supervision and governance platform, comprising,

[0006] The power consumption acquisition module is used to collect power consumption information from users.

[0007] The electricity bill calculation module is used to classify users and then calculate the user's electricity bill according to the corresponding electricity bill calculation rules for each type of user;

[0008] The electricity meter monitoring module is used to monitor the metering data of the user's electricity meter;

[0009] The risk assessment module is used to assess user-side risks based on data output from the electricity billing module and the electricity meter monitoring module.

[0010] The risk governance module is used to retrieve the corresponding governance process from the database based on the assessment results of the risk assessment module and output it.

[0011] A control method for the aforementioned electricity and electricity cost risk internal control and supervision platform includes the following steps:

[0012] A. The power consumption acquisition module collects the power consumption information from the user's terminal and sends the power consumption information to the electricity bill calculation module;

[0013] B. The electricity bill calculation module classifies users and then calculates the user's electricity bill according to the corresponding electricity bill calculation rules for each type of user;

[0014] C. The meter monitoring module monitors the metering data of the user-end meter in step A;

[0015] D. The risk assessment module assesses the user-side risks based on the data output by the electricity bill calculation module and the electricity meter monitoring module;

[0016] E. The risk governance module retrieves the corresponding governance process from the database based on the assessment results of the risk assessment module and outputs it.

[0017] As a preferred option, in step B, users are divided into high-voltage users and low-voltage users.

[0018] As a preferred option, in step C,

[0019] For high-voltage users, their metering code sequence is extracted, and then the feature sequence of the metering code sequence is extracted. The extracted feature sequence is compared with the pre-stored typical feature sequence of high-voltage users. If the linearity exceeds the set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal.

[0020] For low-voltage users, their metering code sequence is extracted, and a metering code simulation curve is fitted. If the deviation between the daily average fluctuation range and the monthly average fluctuation range of the metering code simulation curve is less than a set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal.

[0021] Preferably, when there is a logical topological association between high-voltage users and low-voltage users, if a high-voltage user is determined to be abnormal, then all low-voltage users associated with it will also be determined to be abnormal.

[0022] As a preferred option, in step D,

[0023] When the meter monitoring module detects an anomaly, it determines the corresponding user terminal as a metering risk point.

[0024] When the daily electricity bill change rate or monthly electricity bill change rate calculated by the electricity bill calculation module exceeds the set threshold, the corresponding user terminal is identified as a risk point in electricity consumption.

[0025] Preferably, in step E...

[0026] For metering risk points, their power lines and meters should be inspected and repaired;

[0027] For those with high electricity consumption risk, their credit rating will be downgraded.

[0028] The beneficial effects of adopting the above technical solution are as follows: This invention classifies users and uses different methods to quickly and accurately monitor their metering data according to different categories of users. Finally, it provides corresponding governance processes based on the monitoring results, thereby accurately identifying and managing risky users and reducing the risk to power companies while ensuring the electricity experience of the vast majority of high-quality users. Attached Figure Description

[0029] Figure 1 This is a system schematic diagram of a specific embodiment of the present invention. Detailed Implementation

[0030] Reference Figure 1 One specific embodiment of the present invention includes,

[0031] Power consumption acquisition module 1 is used to collect power consumption information from users.

[0032] Electricity bill calculation module 2 is used to classify users and then calculate the user's electricity bill according to the electricity bill calculation rules corresponding to each type of user;

[0033] The electricity meter monitoring module 3 is used to monitor the metering data of the user's electricity meter;

[0034] Risk assessment module 4 is used to assess user-end risks based on the data output by electricity bill calculation module 2 and electricity meter monitoring module 3;

[0035] Risk governance module 5 is used to retrieve the corresponding governance process from the database based on the assessment results of risk assessment module 4 and output it.

[0036] A control method for the aforementioned electricity and electricity cost risk internal control and supervision platform includes the following steps:

[0037] A. The power consumption acquisition module 1 collects the power consumption information from the user terminal and sends the power consumption information to the electricity bill calculation module 2;

[0038] B. Electricity bill calculation module 2 classifies users into high-voltage users and low-voltage users, and then calculates the user's electricity bill according to the corresponding electricity bill calculation rules for each type of user.

[0039] C. The meter monitoring module 3 monitors the metering data of the user-end meter in step A;

[0040] For high-voltage users, their metering code sequence is extracted, and then the feature sequence of the metering code sequence is extracted. The extracted feature sequence is compared with the pre-stored typical feature sequence of high-voltage users. If the linearity exceeds the set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal.

[0041] For low-voltage users, extract their metering code sequence and fit a metering code simulation curve. If the deviation between the daily average fluctuation range and the monthly average fluctuation range of the metering code simulation curve is less than a set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal.

[0042] When there is a logical topological association between high-voltage users and low-voltage users, if a high-voltage user is determined to be abnormal, then all low-voltage users associated with it will also be determined to be abnormal.

[0043] D. Risk assessment module 4 assesses user-end risks based on the data output by electricity bill calculation module 2 and electricity meter monitoring module 3;

[0044] When the meter monitoring module 3 detects an anomaly, it determines the corresponding user terminal as a metering risk point.

[0045] When the daily electricity bill change rate or monthly electricity bill change rate calculated by the electricity bill calculation module 2 exceeds the set threshold, the corresponding user terminal is determined to be a point of electricity consumption risk.

[0046] E. Risk governance module 5 retrieves the corresponding governance process from the database based on the assessment results of risk assessment module 4 and outputs it.

[0047] For metering risk points, their power lines and meters should be inspected and repaired;

[0048] For those with high electricity consumption risk, their credit rating will be downgraded.

[0049] The monitoring and judgment process of this invention is simple, has low computational load, and has high accuracy in identifying risk users. It does not require significant improvements to the existing power grid monitoring system and is very suitable for upgrading and transforming the existing power grid monitoring system.

[0050] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A control method for an internal control and supervision platform for electricity and electricity cost risks, wherein the internal control and supervision platform for electricity and electricity cost risks includes, The power consumption acquisition module (1) is used to collect power consumption information from the user terminal; The electricity bill calculation module (2) is used to classify users and then calculate the user's electricity bill according to the electricity bill calculation rules corresponding to each type of user; The meter monitoring module (3) is used to monitor the metering data of the user's electricity meter; The risk assessment module (4) is used to assess the user-end risk based on the data output by the electricity bill calculation module (2) and the electricity meter monitoring module (3); The risk governance module (5) is used to retrieve the corresponding governance process from the database based on the assessment results of the risk assessment module (4) and output it. Its characteristics include the following steps: A. Power consumption acquisition module (1) collects power consumption information from the user terminal and sends the power consumption information to the electricity bill calculation module (2). B. Electricity bill calculation module (2) classifies users into high-voltage users and low-voltage users, and then calculates the user's electricity bill according to the electricity bill calculation rules corresponding to each type of user. C. The meter monitoring module (3) monitors the metering data of the user-end meter in step A; For high-voltage users, their metering code sequence is extracted, and then the feature sequence of the metering code sequence is extracted. The extracted feature sequence is compared with the pre-stored typical feature sequence of high-voltage users. If the linearity exceeds the set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal. For low-voltage users, extract their metering code sequence and fit a metering code simulation curve. If the deviation between the daily average fluctuation range and the monthly average fluctuation range of the metering code simulation curve is less than a set threshold, the metering data of this user's electricity meter is determined to be normal; otherwise, the metering data of this user's electricity meter is determined to be abnormal. When there is a logical topological association between high-voltage users and low-voltage users, if a high-voltage user is determined to be abnormal, then all low-voltage users associated with it will also be determined to be abnormal. D. Risk assessment module (4) assesses the user-end risk based on the data output by the electricity bill calculation module (2) and the electricity meter monitoring module (3); E. The risk governance module (5) retrieves the corresponding governance process from the database based on the assessment results of the risk assessment module (4) and outputs it.

2. The control method of the electricity and electricity cost risk internal control supervision and management platform according to claim 1, characterized in that: In step D, When the meter monitoring module (3) detects an anomaly, it determines the corresponding user terminal as a metering risk point. When the daily electricity bill change rate or monthly electricity bill change rate calculated by the electricity bill calculation module (2) exceeds the set threshold, the corresponding user terminal is determined to be a risk point of electricity consumption.

3. The control method of the electricity and electricity cost risk internal control supervision and management platform according to claim 2, characterized in that: In step E, For metering risk points, their power lines and meters should be inspected and repaired; For those with high electricity consumption risk, their credit rating will be downgraded.

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