Marketing electricity price and electricity charge service risk management and control system

Abstract

The invention relates to a marketing electricity price and electricity charge service risk management and control system. The system comprises a data layer, an analysis layer, a risk assessment layer, a control layer and a display layer. The data layer comprises a data acquisition unit and a data storage unit; the analysis layer identifies potential risks through data analysis, including predicting user arrearage risks based on historical payment behaviors and credit scores, using an anomaly detection algorithm to identify zero electricity charge abnormal conditions, analyzing penalty collection conditions and evaluating the influence of the penalty collection conditions on cash flows; the risk assessment layer uses Monte Carlo simulation to quantify the possibility and influence of the risk; the control layer implements a credit scoring system, regularly evaluates the credit state of a customer, and formulates differentiated credit policies and contract terms for implementation according to credit scores; and the display layer is used for establishing a real-time instrument board by using a visual tool and displaying a risk analysis result and key performance indexes. According to the invention, the risk is effectively managed in the electricity price and electricity charge service, and the operation efficiency and customer satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the field of risk management and control, and in particular to a risk management and control system for marketing electricity price and electricity fee services. Background Art

[0002] In the modern electricity market, the management of electricity prices and charges has become increasingly complex, mainly due to market fluctuations, diverse customer behavior, and uncertain economic conditions. With the popularization of smart meters and IoT technologies, power grid companies are able to obtain a large amount of real-time data, including customer electricity behavior, market price changes, and economic indicators. This data provides companies with unprecedented opportunities to optimize electricity price and charge management, but it also brings challenges in data processing and risk management.

[0003] To meet these challenges, power grid companies need a comprehensive risk management system that can collect information from multiple data sources, conduct in-depth data analysis and risk assessment, and manage customer credit and contract terms through intelligent control measures. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a marketing electricity price and electricity fee service risk management system to effectively realize the risk management of electricity price and electricity fee services and improve the competitiveness and risk resistance of the system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A marketing electricity price and electricity fee service risk management and control system, comprising a data layer, an analysis layer, a risk assessment layer, a control layer and a display layer;

[0007] The data layer includes a data acquisition unit and a data storage unit; the data acquisition unit obtains market electricity prices, electricity fee history data, customer electricity consumption behavior data and economic indicators from smart meters, market databases and economic data, and uses API and streaming data processing tools to achieve real-time or regular data acquisition; the data storage unit uses distributed storage for large-scale data and uses a relational database to store structured data and customer information;

[0008] The analysis layer identifies potential risks through data analysis, including predicting the risk of user defaults based on historical payment behavior and credit scores, identifying zero electricity fee anomalies using anomaly detection algorithms, and analyzing the collection of liquidated damages to assess their impact on cash flow;

[0009] The risk assessment layer uses Monte Carlo simulation to quantify the likelihood and impact of risks;

[0010] The control layer implements a credit scoring system, regularly evaluates the credit status of customers, and formulates differentiated credit policies and contract terms based on credit scores;

[0011] The presentation layer uses visualization tools to build real-time dashboards to display risk analysis results and key performance indicators.

[0012] Furthermore, based on historical payment behavior and credit score, the user's arrears risk is predicted as follows: Using the logistic regression model as the basic algorithm, given the feature vector x = [x1, x2, ..., x n ] and weight vector w = [w1, w2, ..., w n ], the decision function of the logistic regression model is:

[0013]

[0014] Where b is the bias term;

[0015] The logistic function uses the Sigmoid function:

[0016]

[0017] This function converts z into a probability in the range [0,1], which predicts the probability that the user is in arrears.

[0018] Furthermore, an anomaly detection algorithm is used to identify the zero electricity fee anomaly, as follows:

[0019] Collect users' electricity bill data and handle missing values ​​and outliers;

[0020] Select features related to electricity charges, including electricity consumption, historical payment records, and billing cycles, and construct derived features, such as the ratio of electricity consumption to bill amount, to obtain a feature data set; standardize the features to make them have the same scale;

[0021] The best k value is selected through cross-validation, and the LOF value is calculated for each data point to identify outliers. For a data point p, its local reachability density is defined as the inverse of the reachable distance of its k nearest neighbors:

[0022]

[0023] Among them, erach-dist k (p, o) is the reachable distance from point p to point o, defined as:

[0024] reach-dist k (p, o) = max (k-distance (o), distance (p, o));

[0025] Among them, k-distance(o) is the distance of the kth nearest neighbor of point o;

[0026] The LOF value is defined as the average value of the ratio of the local reachability density of all points in the neighborhood of point p to the local reachability density of point p:

[0027]

[0028] If LOF(p) is greater than 1, point p is considered to be a potential outlier;

[0029] Set a LOF threshold, points exceeding this value are considered outliers, and identify outliers based on the set threshold;

[0030] Analyze the identified abnormal points to verify whether they are zero electricity fee anomalies.

[0031] Furthermore, Monte Carlo simulation is used to quantify the likelihood and impact of risks, as follows:

[0032] Identify the key factors that affect electricity price and fee risks, including market electricity price fluctuations, demand changes, macroeconomic indicators, etc.; collect historical data to understand the distribution of these variables, including historical electricity price data, customer demand data, and economic indicators; and use descriptive statistical analysis tools to determine the basic distribution of the data.

[0033] Based on data analysis, select the probability distribution of each input variable; and use the fitting function of the SciPy library to select the best probability distribution; estimate the parameters of each probability distribution through historical data;

[0034] Perform Monte Carlo simulation programming based on the probability distribution of the model input variables;

[0035] Identify and quantify risks through analytical results.

[0036] Further, Monte Carlo simulation programming is as follows:

[0037] Generate random samples of input variables using a random number generator according to a chosen probability distribution;

[0038] Run a set number of simulations to generate market behavior scenarios;

[0039] Each simulation calculates the output results, accumulates the results under each scenario, and establishes the probability distribution of the results. The results of each round of simulation are counted, and the frequency distribution of profits and losses and key risk indicators are calculated;

[0040] Calculate confidence intervals for potential losses or profits at different confidence levels to determine the probability and possible impact of potential risk events.

[0041] Furthermore, differentiated credit policies and contract terms are formulated and implemented based on credit scores, as follows:

[0042] Categorize customers into high, medium, and low risk categories based on credit scores; formulate differentiated credit policies for customers of different levels, including credit limits, interest rates, and payment terms;

[0043] Based on the customer's credit rating, adjust the contract terms to reflect the risk level and implement corresponding terms for different customer levels.

[0044] The present invention has the following beneficial effects:

[0045] 1. The present invention provides a comprehensive risk management solution by integrating data collection, analysis, evaluation, control and display functions, so as to effectively manage risks in electricity price and electricity fee services and improve operational efficiency and customer satisfaction;

[0046] 2. The close integration between the various layers of the present invention ensures the high efficiency of data flow and information sharing. The system design has good flexibility and scalability, and can adapt to market changes and adjustments to business needs. Through comprehensive data analysis and risk assessment, it supports enterprises to make more informed decisions, reduce risks and increase profits.

[0047] 3. The present invention comprehensively identifies potential risks by analyzing historical payment behaviors, credit scores and anomaly detection, provides a multi-dimensional risk perspective, uses advanced algorithms to predict user arrears risks and identify abnormal situations of zero electricity bills, improves the accuracy of risk prediction, and quantifies the possibility and impact of risks through Monte Carlo simulation, providing data-driven risk assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0050] refer to Figure 1 , in this embodiment, a marketing electricity price and electricity fee service risk management system is provided, including a data layer, an analysis layer, a risk assessment layer, a control layer and a display layer;

[0051] The data layer includes a data acquisition unit and a data storage unit; the data acquisition unit obtains market electricity prices, electricity fee history data, customer electricity consumption behavior data and economic indicators from smart meters, market databases and economic data, and uses API and streaming data processing tools to achieve real-time or regular data acquisition; the data storage unit uses distributed storage for large-scale data and uses a relational database to store structured data and customer information;

[0052] The analysis layer identifies potential risks through data analysis, including predicting the risk of user defaults based on historical payment behavior and credit scores, identifying zero electricity fee anomalies using anomaly detection algorithms, and analyzing the collection of liquidated damages to assess their impact on cash flow;

[0053] The risk assessment layer uses Monte Carlo simulation to quantify the likelihood and impact of risks;

[0054] The control layer implements a credit scoring system, regularly evaluates the credit status of customers, and formulates differentiated credit policies and contract terms based on credit scores;

[0055] The presentation layer uses visualization tools to build real-time dashboards to display risk analysis results and key performance indicators.

[0056] In this embodiment, based on historical payment behavior and credit score, the risk of user default is predicted as follows: Using the logistic regression model as the basic algorithm, given the feature vector x = [x1, x2, ..., x n ] and weight vector w = [w1, w2, ..., w n ], the decision function of the logistic regression model is:

[0057]

[0058] Where b is the bias term;

[0059] The logistic function uses the Sigmoid function:

[0060]

[0061] This function converts z into a probability in the range [0,1], which predicts the probability that the user is in arrears.

[0062] In this embodiment, an anomaly detection algorithm is used to identify the zero electricity fee anomaly, as follows:

[0063] Collect users' electricity bill data and handle missing values ​​and outliers;

[0064] Select features related to electricity charges, including electricity consumption, historical payment records, and billing cycles, and construct derived features, such as the ratio of electricity consumption to bill amount, to obtain a feature data set; standardize the features to make them have the same scale;

[0065] The best k value is selected through cross-validation, and the LOF value is calculated for each data point to identify outliers. For a data point p, its local reachability density is defined as the inverse of the reachable distance of its k nearest neighbors:

[0066]

[0067] Among them, reach-dist k (p, o) is the reachable distance from point p to point o, defined as:

[0068] reach-dist k (p, o) = max (k-distance (o), distance (p, o));

[0069] Among them, k-distance(o) is the distance of the kth nearest neighbor of point o;

[0070] The LOF value is defined as the average value of the ratio of the local reachability density of all points in the neighborhood of point p to the local reachability density of point p:

[0071]

[0072] If LOF(p) is greater than 1, point p is considered to be a potential outlier;

[0073] Set a LOF threshold, points exceeding this value are considered outliers, and identify outliers based on the set threshold;

[0074] Analyze the identified abnormal points to verify whether they are zero electricity fee anomalies.

[0075] In this example, Monte Carlo simulation is used to quantify the likelihood and impact of risk, as follows:

[0076] Identify the key factors that affect electricity price and fee risks, including market electricity price fluctuations, demand changes, macroeconomic indicators, etc.; collect historical data to understand the distribution of these variables, including historical electricity price data, customer demand data, and economic indicators; and use descriptive statistical analysis tools to determine the basic distribution of the data.

[0077] Based on data analysis, select the probability distribution of each input variable (for example, electricity prices may follow a normal distribution); and use the SciPy library's fitting function to select the best probability distribution; estimate the parameters of each probability distribution (such as mean, standard deviation) through historical data;

[0078] Perform Monte Carlo simulation programming based on the probability distribution of the model input variables;

[0079] Identify and quantify risks through analytical results.

[0080] In this embodiment, the Monte Carlo simulation programming is as follows:

[0081] Use a random number generator (such as Python's random, numpy.random) to generate random samples of the input variables according to the chosen probability distribution;

[0082] Run a set number of simulations (e.g. 10,000) to generate market behavior scenarios;

[0083] Output the results of each simulation, accumulate the results of each scenario, and establish the probability distribution of the results. Count the results of each round of simulation, calculate the frequency distribution of profits and losses and key risk indicators (such as mean, variance, and extreme values);

[0084] Calculate the confidence interval of potential losses or profits at different confidence levels (such as 5% and 95% quantiles) to determine the probability and possible impact of potential risk events.

[0085] In this embodiment, differentiated credit policies and contract terms are formulated and implemented according to credit scores, as follows:

[0086] Categorize customers into high, medium, and low risk categories based on credit scores; formulate differentiated credit policies for customers of different levels, including credit limits, interest rates, and payment terms;

[0087] Adjust contract terms to reflect risk levels based on customer credit ratings, and implement corresponding terms for different customer levels

[0088] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A marketing electricity price and electricity fee service risk management system, characterized in that: It includes data layer, analysis layer, risk assessment layer, control layer and display layer; The data layer includes a data acquisition unit and a data storage unit; the data acquisition unit obtains market electricity prices, electricity fee history data, customer electricity consumption behavior data and economic indicators from smart meters, market databases and economic data, and uses API and streaming data processing tools to achieve real-time or regular data acquisition; the data storage unit uses distributed storage for large-scale data and uses a relational database to store structured data and customer information; The analysis layer identifies potential risks through data analysis, including predicting the risk of user defaults based on historical payment behavior and credit scores, identifying zero electricity fee anomalies using anomaly detection algorithms, and analyzing the collection of liquidated damages to assess their impact on cash flow; The risk assessment layer uses Monte Carlo simulation to quantify the likelihood and impact of risks; The control layer implements a credit scoring system, regularly evaluates the credit status of customers, and formulates differentiated credit policies and contract terms based on credit scores; The presentation layer uses visualization tools to build real-time dashboards to display risk analysis results and key performance indicators.

2. A marketing electricity price and electricity fee service risk management and control system according to claim 1, characterized in that: The method of predicting the user's arrears risk based on historical payment behavior and credit score is as follows: Using the logistic regression model as the basic algorithm, given the feature vector x = [x1, x2, ..., x n ] and weight vector w = [w1, w2, ..., w n ], the decision function of the logistic regression model is: Where b is the bias term; The logistic function uses the Sigmoid function: This function converts z into a probability in the range [0,1], which predicts the probability that the user is in arrears.

3. A marketing electricity price and electricity fee service risk management and control system according to claim 1, characterized in that: The anomaly detection algorithm is used to identify the zero electricity fee anomaly, as follows: Collect users' electricity bill data and handle missing values ​​and outliers; Select features related to electricity charges, including electricity consumption, historical payment records, and billing cycles, and construct derived features, such as the ratio of electricity consumption to bill amount, to obtain a feature data set; standardize the features to make them have the same scale; The best k value is selected through cross-validation, and the LOF value is calculated for each data point to identify outliers. For a data point p, its local reachability density is defined as the inverse of the reachable distance of its k nearest neighbors: Among them, reach-dist k (p, o) is the reachable distance from point p to point o, defined as: reach-dist k (p,o)=max(k-distance(o),distance(p,o); Among them, k-distance(o) is the distance of the kth nearest neighbor of point o; The LOF value is defined as the average value of the ratio of the local reachability density of all points in the neighborhood of point p to the local reachability density of point p: If LOF(p) is greater than 1, point p is considered to be a potential outlier; Set a LOF threshold, points exceeding this value are considered outliers, and identify outliers based on the set threshold; Analyze the identified abnormal points to verify whether they are zero electricity fee anomalies.

4. A marketing electricity price and electricity fee service risk management and control system according to claim 1, characterized in that: The Monte Carlo simulation is used to quantify the likelihood and impact of risks as follows: Identify the key factors that affect electricity price and fee risks, including market electricity price fluctuations, demand changes, macroeconomic indicators, etc.; collect historical data to understand the distribution of these variables, including historical electricity price data, customer demand data, and economic indicators; and use descriptive statistical analysis tools to determine the basic distribution of the data. Based on data analysis, select the probability distribution of each input variable; And use the fitting function of the SciPy library to select the best probability distribution; estimate the parameters of each probability distribution through historical data; Perform Monte Carlo simulation programming based on the probability distribution of the model input variables; Identify and quantify risks through analytical results.

5. A marketing electricity price and electricity fee service risk management and control system according to claim 4, characterized in that: The Monte Carlo simulation programming is as follows: Generate random samples of input variables using a random number generator according to a chosen probability distribution; Run a set number of simulations to generate market behavior scenarios; Each simulation calculates the output results, accumulates the results under each scenario, and establishes the probability distribution of the results. The results of each round of simulation are counted, and the frequency distribution of profits and losses and key risk indicators are calculated; Calculate confidence intervals for potential losses or profits at different confidence levels to determine the probability and possible impact of potential risk events.

6. A marketing electricity price and electricity fee service risk management and control system according to claim 1, characterized in that: The differentiated credit policies and contract terms formulated based on credit scores are implemented as follows: Categorize customers into high, medium, and low risk categories based on credit scores; formulate differentiated credit policies for customers of different levels, including credit limits, interest rates, and payment terms; Based on the customer's credit rating, adjust the contract terms to reflect the risk level and implement corresponding terms for different customer levels.

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