Electric power marketing business application system integrating artificial intelligence and business process automation

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an electric power marketing business application system integrating artificial intelligence and business process automation. According to the system, customer information is automatically collected and managed through the customer management module, errors caused by manual processing are eliminated, the market analysis module adopts a moving average denoising technology, so that the market share and the customer wastage rate are calculated more accurately, and the sales prediction module combines historical electricity consumption data and seasonal factors to predict the market share and the customer wastage rate. The risk management module efficiently calculates a customer credit score and a market fluctuation risk value based on historical data by using an artificial intelligence technology, timely identifies and evaluates potential risks and reduces the possibility of decision errors, and the comprehensive report module integrates data of all the modules, so that the risk management module can report the risk value of the customer credit score and the market fluctuation risk value. Through the system integration, the automation and the accuracy of data processing are improved, and the overall efficiency and the market competitiveness of the power marketing business are also improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically to a power marketing business application system integrating artificial intelligence and business process automation. Background Art

[0002] With the continuous development and increasing competition in the power market, power marketing business is facing more and more complex challenges. In order to improve market share and customer satisfaction, power enterprises urgently need a marketing business application system integrating artificial intelligence and business process automation. Such a system can not only efficiently manage customer information and electricity consumption data, but also, through in-depth market analysis and sales forecasting, help enterprises formulate scientific and reasonable marketing strategies. This integrated solution can significantly improve the accuracy of decision-making, reduce operating costs, and enhance the market competitiveness of enterprises, so as to remain invincible in the dynamically changing power market.

[0003] However, there are still many deficiencies in the existing power marketing systems in terms of data processing and risk management. Especially in the process of collecting and analyzing customer information, in order to accurately evaluate customer credit and market risks, it often relies on manual means, which is prone to inaccurate data processing and decision-making errors. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides a power marketing business application system integrating artificial intelligence and business process automation. The customer management module automatically collects and manages customer information to ensure the accuracy and integrity of data, eliminating errors caused by manual processing. The market analysis module adopts the moving average denoising technology to improve the accuracy of data analysis, so as to more accurately calculate the market share and customer churn rate. The sales forecasting module combines historical electricity consumption data and seasonal factors to provide more accurate electricity demand forecasts, helping to formulate scientific and reasonable marketing strategies. The risk management module uses artificial intelligence technology to efficiently calculate customer credit scores and market volatility risk values based on historical data, timely identify and evaluate potential risks, and reduce the possibility of decision-making errors. The comprehensive report module integrates the data of each module to generate a comprehensive power marketing business analysis report, providing decision-making support for management personnel. Through such system integration, not only the automation and accuracy of data processing are improved, but also the scientificity and effectiveness of risk management are significantly enhanced, thus improving the overall efficiency and market competitiveness of power marketing business.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A power marketing business application system integrating artificial intelligence and business process automation, including a customer management module, a market analysis module, a sales forecasting module, a marketing planning module, a risk management module, and a comprehensive report module;

[0008] The customer management module is used to collect and manage customer information, including customer basic information, electricity consumption history records, charge records, and customer feedback data, and transmit it to the market analysis module;

[0009] After the market analysis module performs moving average denoising and eliminates invalid data on the data transmitted by the customer management module, it conducts market analysis by calculating market share, customer churn rate, and competitor pricing trends, and the market analysis results are sent to the sales forecasting module;

[0010] Based on the market analysis results, the sales forecasting module combines the user's historical electricity consumption data and the season in which they are located to predict the future electricity consumption demand of users, calculates the sales growth rate and the predicted value of the power market business demand, and transmits it to the marketing planning module;

[0011] The marketing planning module formulates future power marketing business strategies based on the values calculated by the sales forecasting module, generates a general plan for marketing strategies, and sends it to the risk management module;

[0012] After receiving the general plan for marketing strategies, the risk management module calculates the customer credit score and the market volatility risk value based on historical data obtained by artificial intelligence, and accordingly identifies and evaluates market risks and credit risks;

[0013] The comprehensive report module integrates the data of all the above modules, generates a comprehensive analysis report on power marketing business, and sends it to the display terminal of the management personnel.

[0014] Preferably, the formula for moving average denoising of the data is as follows:

[0015]

[0016] In the formula, SMA n represents the simple moving average value at the nth moment, N represents the selected moving window size, x n-i represents the original data value at the (n - i)th moment, and i represents the counting subscript.

[0017] Preferably, the formula for eliminating invalid data is as follows:

[0018]

[0019] In the formula, x represents the data point to be processed, L represents the low threshold, H represents the high threshold, Valid(x) represents returning a valid data point, and NaN represents an invalid data point.

[0020] Preferably, the formula for calculating the market share is as follows:

[0021]

[0022] In the formula, Makt represents the market share, Gsxs represents the company's sales, and Scxs represents the total sales of the entire market.

[0023] Preferably, the calculation formula for the customer churn rate is as follows:

[0024]

[0025] In the formula, ChRt represents the customer churn rate, Ls represents the number of customers who stopped using the service, and Qc represents the number of customers at the beginning of the period.

[0026] Preferably, the formula for analyzing the pricing trend of competitors is as follows:

[0027]

[0028] In the formula, PcRt represents the pricing analysis trend, P t represents the price at the current moment, and P t-1 represents the price in the previous time period.

[0029] Preferably, the formula for calculating the sales growth rate is as follows:

[0030]

[0031] In the formula, Salt represents the sales growth rate, Bq represents the total sales of the current period, and Sq represents the total sales of the previous period.

[0032] Preferably, the formula for calculating the predicted value of the power market business demand is as follows:

[0033] D n = α * U n + β * S + ∈

[0034] In the formula, D n represents the predicted power business demand for the nth period, U n represents the historical electricity consumption for the nth period, S represents the season number, α and β represent model parameters obtained through artificial intelligence analysis, and ∈ represents the random error of the model prediction.

[0035] Preferably, the formula for calculating the customer credit score is as follows:

[0036] CS = w1 * PH + w2 * CU + w3 * LH

[0037] In the formula, CS represents the customer credit score, PH represents the customer payment history score, CU represents the customer credit utilization rate, LH represents the length of the customer credit record, and w1, w2, and w3 represent the weights of each factor, which are automatically assigned by artificial intelligence.

[0038] Preferably, the formula for calculating the market volatility risk value is as follows:

[0039]

[0040] In the formula, MRV represents the market volatility risk value, σ d represents the standard deviation of the market demand change, and μ d represents the mean value of the market demand, which is obtained by automatic analysis of artificial intelligence.

[0041] Compared with the prior art, the present invention provides a power marketing business application system integrating artificial intelligence and business process automation, which has the following beneficial effects:

[0042] The present invention automatically collects and manages customer information through the customer management module to ensure the accuracy and integrity of data, eliminates errors caused by manual processing. The market analysis module adopts the moving average denoising technology to improve the accuracy of data analysis, so as to calculate the market share and customer churn rate more accurately. The sales forecasting module combines historical electricity consumption data and seasonal factors to provide a more accurate electricity demand forecast, helping to formulate a scientific and reasonable marketing strategy. The risk management module uses artificial intelligence technology to efficiently calculate the customer credit score and market volatility risk value based on historical data, timely identify and evaluate potential risks, and reduce the possibility of decision-making errors. Finally, the comprehensive report module integrates the data of each module to generate a comprehensive power marketing business analysis report, providing decision-making support for managers. Through such system integration, not only the automation and accuracy of data processing are improved, but also the scientificity and effectiveness of risk management are significantly enhanced, thereby improving the overall efficiency and market competitiveness of power marketing business. Brief Description of the Drawings

[0043] Figure 1 It is a schematic diagram of the system flow of the present invention. Detailed Embodiment

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] In view of the fact that there are still many deficiencies in the existing power marketing system in terms of data processing and risk management, especially in the process of collecting and analyzing customer information, in order to accurately evaluate customer credit and market risks, it often relies on manual means, which is prone to problems such as inaccurate data processing and decision-making errors. Therefore, a power marketing business application system integrating artificial intelligence and business process automation is proposed. Please refer to Figure 1 , this system includes a customer management module, a market analysis module, a sales forecasting module, a marketing planning module, a risk management module, and a comprehensive report module;

[0046] The customer management module is the core component of the power marketing business application system, aiming to comprehensively collect and efficiently manage customer information through a variety of advanced technical means. This module adopts a cloud computing-based customer relationship management (CRM) system, which can store and process customer basic information in real time, such as name, address, contact information, and account status. To ensure data security, the system uses encryption technology to protect customer sensitive information and adopts a secure transport layer protocol (TLS) during transmission to prevent data leakage;

[0047] In terms of electricity consumption history records, the module automatically collects customers' electricity consumption data through intelligent metering devices (such as smart meters), generates detailed electricity consumption curves, and can analyze electricity consumption patterns in different time periods. These data not only include real-time electricity consumption, but also cover customers' electricity consumption behaviors during peak and off-peak periods, providing a data basis for subsequent analysis;

[0048] The charge records are managed through an integrated charge management system. The system will record the detailed information of each transaction, including the charge time, amount, and payment method, and at the same time support reconciliation and bill generation. This process greatly reduces manual operation errors and improves the transparency and accuracy of customer accounts;

[0049] In addition, the collection of customer feedback data relies on online survey tools and social media monitoring technologies. Users can submit suggestions and complaints through various channels (such as APP, official website, or social media). The module can analyze these feedback information in real time, extract the themes and emotions of customer opinions through natural language processing technology (NLP), so as to help enterprises better understand customer needs and improve service quality;

[0050] The market analysis module is an indispensable part of the power marketing business application system. It is responsible for deeply analyzing the data transmitted from the customer management module. First of all, this module uses the moving average denoising technique to process a large amount of customer data to reduce the noise caused by short-term fluctuations, so as to extract more stable trend information. Specifically, the formula of simple moving average (SMA) is adopted:

[0051]

[0052] Among them, N is the selected window size, and x n-i is the original data at the corresponding time point. Through this method, the market analysis module can make the data analysis results more accurate, helping to ensure that subsequent decisions are based on real market trends rather than accidental fluctuations;

[0053] Secondly, the process of eliminating invalid data is based on statistical principles. By setting thresholds, the data beyond the thresholds is excluded. The specific formula is:

[0054]

[0055] In the formula, x represents the data point to be processed, L represents the low threshold, H represents the high threshold, Valid(x) represents returning the valid data point, and NaN represents the invalid data point. By using these two methods, the market analysis module ensures the quality of the incoming data, thus providing more reliable analysis results;

[0056] Based on the valid data, the market analysis module further calculates the market share, customer churn rate, and competitor pricing trend. The formula for calculating the market share is:

[0057]

[0058] The customer churn rate is calculated in the following way:

[0059]

[0060] In the formula, ChRt represents the customer churn rate, Ls represents the number of customers who stop using the service, and Qc represents the number of customers at the beginning of the period. At the same time, the evaluation of the competitor pricing trend is achieved through the price change rate formula:

[0061]

[0062] The benefits of these calculations are that enterprises can monitor market dynamics in real time, identify potential risks and opportunities, adjust marketing strategies and business models in a timely manner to enhance market competitiveness. Finally, through precise data processing and analysis, the market analysis module will comprehensively send the obtained results to the sales forecasting module, providing a scientific basis for estimating the future demand in the electricity market and ensuring that enterprises can take the initiative in the market competition;

[0063] The sales forecasting module is responsible for accurately predicting the future electricity demand of users based on the market analysis results, combined with the users' historical electricity consumption data and corresponding seasonal factors. This module first uses machine learning algorithms (such as regression analysis, time series analysis, and seasonal decomposition) to identify and learn the patterns of users' electricity consumption behavior in order to more accurately predict future demand. By inputting key indicators such as market share and customer churn rate provided by the market analysis module, the sales forecasting module can comprehensively understand the current market environment, thereby enhancing the prediction accuracy;

[0064] In terms of specific calculations, the sales forecasting module uses the following formula to estimate future electricity demand and sales growth rate:

[0065] The formula for calculating the sales growth rate is:

[0066]

[0067] This formula can reflect the magnitude of business growth by comparing current sales with previous sales data, providing an important decision-making basis for management;

[0068] When predicting users' electricity demand, by constructing a multiple linear regression model, the sales forecasting module uses the following formula:

[0069] D n =α*U n +β*S+∈

[0070] In the formula, D n represents the electricity business demand in the nth predicted period, U nDenote the historical electricity consumption in the nth period, S represents the season number, and the setting of the season number is usually based on historical data analysis by artificial intelligence. Due to the use of cooling equipment such as air conditioners, the electricity consumption is usually high. Therefore, the number for summer can be set to a relatively large value of 1.2. Due to the use of heating equipment, the electricity consumption may also be high, but it may be different from that in summer. Therefore, the number for winter is set to 1.1. The electricity consumption in spring and autumn is usually low, so the numbers can be set to relatively small values of 0.9 and 0.8. α and β represent model parameters obtained through artificial intelligence analysis, and ∈ represents the random error predicted by the model. In this way, the module not only considers the changes in users' historical data but also factors in the impact of seasonality on electricity demand, thus achieving high-accuracy demand prediction. Such a prediction will help enterprises anticipate future changes in electricity demand and contribute to the rational planning of electricity production and supply;

[0071] By introducing the season number as above, the impact of different seasons on electricity demand can be captured more accurately. The setting of the season number is analyzed by artificial intelligence based on historical data, which enables the model to more realistically reflect the impact of seasonal changes on electricity demand, thereby improving the accuracy of prediction;

[0072] The final sales prediction module aggregates and transmits the calculated sales growth rate and the predicted value of the electricity market business demand to the marketing planning module, providing data support for the formulation of subsequent marketing strategies. This not only optimizes resource allocation but also enhances the enterprise's market response ability, enabling it to maintain a competitive advantage in the rapidly changing electricity market;

[0073] The marketing planning module formulates marketing business strategies for the future electricity market based on the sales growth rate and the predicted value of market demand calculated by the sales prediction module. This module uses advanced data analysis and decision support systems, combined with machine learning algorithms, to extract key factors from a large amount of historical sales data and market trends, thereby forming a precise marketing strategy. For example, the module can identify the preferences and behavior patterns of specific customer groups and generate a general plan for future marketing strategies through prediction models (such as ARIMA models or seasonal adjustment models);

[0074] The generated general plan for marketing strategies will systematically cover elements such as the target market, promotion plan, and price strategy, and be sent to the risk management module through an automated process to ensure a comprehensive risk assessment before implementation;

[0075] In the risk management module, the received general plan for marketing strategies will be further analyzed. This module uses artificial intelligence technology, combined with big data analysis, to automatically extract and evaluate historical data. By calculating the customer credit score, the formula is:

[0076] CS=w1*PH+w2*CU+w3*LH

[0077] In the formula, CS represents the customer's credit score, PH represents the customer's payment history score, CU represents the customer's credit utilization rate, LH represents the length of the customer's credit record, and w1, w2, and w3 represent the weights of each factor, which are automatically assigned by artificial intelligence. Here, the customer credit score (CS) takes into account multiple factors, such as payment history, credit utilization rate, and credit history length, to reflect the customer's credit risk. By quantifying these factors, the module can effectively identify high-risk customers, thereby reducing the potential default rate;

[0078] The assignment of weight values ​​w1, w2, and w3 is based on historical data analysis and machine learning model training. For example, in the credit card scoring scenario, through linear regression analysis of historical data, it is determined that the weight of payment history is 0.4, the weight of credit utilization is 0.3, and the weight of credit history length is 0.3. In addition, the weight values ​​can be dynamically adjusted according to business goals and market changes, such as optimizing the weight values ​​in real time through reinforcement learning models, thereby improving the accuracy and adaptability of credit scoring;

[0079] In addition, the risk management module also calculates the market volatility risk value, the formula is:

[0080]

[0081] In the formula, MRV represents the market volatility risk value, σ d represents the standard deviation of market demand changes, μ d It represents the mean of market demand, which is automatically obtained through artificial intelligence analysis. By evaluating the volatility of market demand, enterprises can take preventive measures in time to deal with possible market risks.

[0082] Finally, the comprehensive report module will integrate the data generated by each module, including marketing strategies, risk assessment results, customer credit scores and market volatility risks, to generate a detailed comprehensive analysis report on power marketing business for managers. This report is sent to the manager's display terminal through a visual dashboard or traditional text form, helping them to gain an in-depth understanding of business conditions, market dynamics and potential risks, and to facilitate the scientific and precise decision-making process. This comprehensive analysis function not only improves the company's response speed to market changes, but also provides strong data support for future power market strategic planning.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electric power marketing business application system integrating artificial intelligence and business process automation, characterized in that: It includes a customer management module, a market analysis module, a sales forecasting module, a marketing planning module, a risk management module, and a comprehensive report module; The customer management module is used to collect and manage customer information, including customer basic information, electricity consumption history records, charging records, and customer feedback data, and transmit it to the market analysis module; After performing moving average denoising and removing invalid data on the data transmitted by the customer management module, the market analysis module conducts market analysis by calculating market share, customer churn rate, and competitor pricing trends, and the market analysis results are sent to the sales forecasting module; Based on the market analysis results, combined with the user's historical electricity consumption data and the season, the sales forecasting module forecasts the future electricity consumption demand of users, calculates the sales growth rate and the predicted value of the electricity market business demand, and transmits it to the marketing planning module; The marketing planning module formulates future electricity market marketing business strategies according to the values calculated by the sales forecasting module, generates a general plan for marketing strategies, and sends it to the risk management module; After receiving the general plan for marketing strategies, the risk management module calculates the customer credit score and the market volatility risk value based on artificial intelligence obtaining historical data, and accordingly identifies and evaluates market risks and credit risks; The comprehensive report module integrates the data of all the above modules, generates a comprehensive analysis report on electricity marketing business, and sends it to the display terminal of the management personnel.

2. The power marketing business application system integrating artificial intelligence and business process automation according to claim 1, characterized in that: The formula for performing moving average denoising on the data is as follows: In the formula, SMA n represents the simple moving average at the nth moment, N represents the selected moving window size, and x n-i represents the original data value at the (n - i)th moment, and i represents the counting subscript.

3. The power marketing business application system integrating artificial intelligence and business process automation according to claim 2, characterized in that: The formula for removing invalid data is as follows: In the formula, x represents the data point to be processed, L represents the low threshold, H represents the high threshold, Valid(x) represents returning the valid data point, and NaN represents the invalid data point.

4. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 3, characterized in that: The formula for calculating the market share is as follows: In the formula, Makt represents the market share, Gsxs represents the company's sales, and Scxs represents the total sales of the entire market.

5. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 4, characterized in that: The calculation formula for the customer churn rate is as follows: In the formula, ChRt represents the customer churn rate, Ls represents the number of customers who stop using the service, and Qc represents the number of customers at the beginning of the period.

6. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 5, characterized in that: The formula for analyzing the competitor pricing trend is as follows: In the formula, PcRt represents the pricing analysis trend, and P t represents the price at the current moment, and P t-1 represents the price in the previous time period.

7. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 6, characterized in that: The formula for calculating the sales growth rate is as follows: In the formula, Salt represents the sales growth rate, Bq represents the total sales of the current period, and Sq represents the total sales of the previous period.

8. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 7, characterized in that: The formula for calculating the predicted value of the electricity market business demand is as follows: D n = α * U n + β * S + ∈ In the formula, D n represents the electricity business demand in the predicted nth period, U n represents the historical electricity consumption in the nth period, S represents the season number, α and β represent model parameters obtained through artificial intelligence analysis, and ∈ represents the random error of model prediction.

9. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 8, characterized in that: The formula for calculating the customer credit score is as follows: CS = w1 * PH + w2 * CU + w3 * LH In the formula, CS represents the customer credit score, PH represents the customer payment history score, CU represents the customer credit utilization rate, LH represents the length of time of the customer credit record, and w1, w2, and w3 represent the weights of each factor, which are automatically assigned by artificial intelligence.

10. An electric power marketing business application system integrating artificial intelligence and business process automation according to claim 9, characterized in that: The formula for calculating the market volatility risk value is as follows: In the formula, MRV represents the market volatility risk value, and σ d represents the standard deviation of the market demand change, and μ d represents the mean value of the market demand, which is obtained through automatic analysis by artificial intelligence.

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