Intelligent service optimization method and system for power grid business hall

Abstract

The invention discloses an intelligent service optimization method for a power grid business hall, and the method comprises the steps: collecting the power utilization data, business handling data and customer information data of a customer, and integrating the data, thereby forming a customer data portrait; the collected original data are preprocessed; the preprocessed data are analyzed, and customer demand prediction is carried out according to a customer demand prediction model and a problem early warning model; and according to a customer demand prediction result and a customer data portrait, providing personalized service recommendation, guiding the customer to use an online business platform to handle business, actively processing customer problems, and optimizing business hall resource configuration. The invention further discloses a corresponding system. According to the invention, the service quality and operation efficiency of the power grid business hall can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment monitoring, in particular to a power grid service hall intelligent service optimization method and system. BACKGROUND

[0002] With the rapid development of smart grids and the gradual opening of the electricity market, power grid service halls are facing the dual challenges of increasing customer demand diversification and service quality improvement. The traditional service hall service mode mainly relies on customers actively going to the hall to handle business, which not only leads to large passenger flow in the service hall and long customer waiting time, but also increases operating costs. With the help of big data analysis technology, in-depth mining of customer electricity consumption behavior, business handling records and other data can accurately predict customer demand and potential problems, so as to take measures in advance, reduce the number of customers to the hall or guide customers to handle business through online channels, improve customer service efficiency and satisfaction, and optimize service hall operation management. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a power grid service hall intelligent service optimization method and system, which can improve the service quality and operation efficiency of the power grid service hall.

[0004] To solve the above technical problems, as one aspect of the present application, a power grid service hall intelligent service optimization method is provided, which includes the following steps:

[0005] Collecting customer electricity consumption data, business handling data and customer information data, and integrating the data to form a customer data portrait;

[0006] Preprocessing the collected raw data, which includes cleaning, standardization and missing value processing;

[0007] Analyzing the preprocessed data and predicting customer demand according to a customer demand prediction model and a problem early warning model; the analysis includes electricity consumption trend analysis, electricity consumption pattern recognition, abnormal electricity consumption detection, business handling frequency analysis, business handling time analysis and business handling correlation analysis;

[0008] According to the customer demand prediction results and the customer data portrait, providing personalized service recommendations to guide customers to handle business using online business platforms, actively handling customer problems, and optimizing service hall resource allocation.

[0009] Preferably, the personalized service recommendations include pushing electricity tariff packages suitable for customers' electricity consumption needs, energy-saving product recommendations and electricity safety tips to customers through mobile applications, short messages or emails.

[0010] Preferably, the online service guidance comprises: setting up publicity boards and two-dimensional codes in the business hall, guiding customers to download mobile applications and register to use online service functions, and when customers handle business, staff actively introduce the convenience and advantages of online services and guide customers to operate on site.

[0011] Preferably, the active processing of customer problems comprises: using a customer problem early warning model to discover potential problems existing in customers in time, and communicating with customers to provide solutions or arranging staff to check and repair on site.

[0012] Preferably, the optimal allocation of business hall resources comprises: reasonably arranging personnel scheduling and window settings of the business hall according to customer business handling time analysis results, and optimizing the functional layout of the business hall.

[0013] Correspondingly, another aspect of the present application also provides an intelligent service optimization system for a power grid business hall, comprising:

[0014] A data collection and integration module is configured to collect power consumption data, business handling data and customer information data of customers, and integrate the data to form a customer data portrait.

[0015] A data preprocessing module is configured to clean, standardize and process missing values of the collected original data.

[0016] A big data analysis module is configured to analyze the preprocessed data, and predict customer demand according to a customer demand prediction model and a problem early warning model; the analysis comprises power consumption trend analysis, power consumption mode identification, abnormal power consumption detection, business handling frequency analysis, business handling time analysis and business handling correlation analysis.

[0017] A service optimization processing module is configured to provide personalized service recommendations according to customer demand prediction results and customer data portraits, guide customers to handle business using an online service platform, actively process customer problems, and optimize the allocation of business hall resources.

[0018] Preferably, the service optimization processing module further comprises:

[0019] A personalized service recommendation unit is configured to push electricity tariff packages, energy-saving product recommendations and power consumption safety tips suitable for power consumption demands of customers through mobile applications, short messages or emails.

[0020] An online service guidance unit is configured to set up publicity boards and two-dimensional codes in the business hall, guide customers to download mobile applications and register to use online service functions, and when customers handle business, staff actively introduce the convenience and advantages of online services and guide customers to operate on site.

[0021] The customer problem active processing unit is used for discovering potential problems existing in the customer in time by using a customer problem early warning model, and communicating with the customer to provide a solution or arranging a staff to check and repair on site;

[0022] The business hall resource optimal allocation unit is used for reasonably arranging staff scheduling and window setting of the business hall and optimizing the function layout of the business hall according to the analysis result of the customer business handling time.

[0023] The embodiment of the application has the following beneficial effects:

[0024] The application provides a power grid business hall intelligent service optimization method and system, which can accurately predict customer demand and potential problems by using big data analysis technology, so that the service is more forward-looking and targeted. By actively pushing personalized service recommendations and online business guidance, the number of times and waiting time of customers to the hall are effectively reduced, and the service efficiency is improved. Customers can conveniently handle business through online channels and enjoy more efficient and convenient service experience, thereby improving customer satisfaction.

[0025] According to the analysis result of the customer business handling time, the staff scheduling and window setting of the business hall are reasonably arranged, so that the business hall resources are more reasonably utilized. By optimizing the function layout, the operation and management level of the business hall is improved, so that the business hall can be more efficiently and orderly operated. The operation cost is effectively reduced, and the valuable resources of the power grid enterprise are saved.

[0026] The application provides personalized services and online business guidance, enhances the customer's dependence and stickiness to the power grid business hall, and makes the customer more willing to choose the power grid business hall as their service provider. The implementation of the application improves the competitiveness of the power grid enterprise in the power market, so that the power grid enterprise can attract more customers and expand the business market share.

[0027] The application can timely discover potential safety hazards of power utilization, such as equipment aging and overload, by analyzing customer power utilization equipment data, so as to remind the customer to maintain or repair the equipment. By improving the customer's safety awareness of power utilization, the application helps to reduce safety accidents caused by improper power utilization and ensures the safe operation of the power grid.

[0028] In summary, the application improves the service quality and operation efficiency, enhances the customer stickiness and market competitiveness. At the same time, the application also improves the customer's safety awareness of power utilization, and lays a solid foundation for the sustainable development of the power grid enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0030] Figure 1 This is a schematic diagram of the main process of an embodiment of the intelligent service optimization method for power grid business halls provided by the present invention;

[0031] Figure 2 This is a schematic diagram of a structure of an embodiment of an intelligent service optimization system for power grid business halls provided by the present invention;

[0032] Figure 3 for Figure 2 A schematic diagram of the service optimization processing module in the system. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0034] like Figure 1 The diagram shows the main flow of an embodiment of the intelligent service optimization method for power grid business halls provided by the present invention; in this embodiment, the method includes at least the following steps:

[0035] A method for optimizing intelligent services in power grid business halls is provided, which includes the following steps:

[0036] Step S10: Collect customer electricity consumption data, business processing data, and customer information data, and integrate the data to form a customer data profile;

[0037] In a specific example, in step S10, the following data needs to be collected:

[0038] Electricity consumption data collection: Through devices such as smart meters, customer electricity consumption data is collected in real time, including electricity consumption, usage time, power consumption, power factor, and other information. This data will be transmitted to the power company's data center at regular intervals (e.g., every 15 minutes).

[0039] Business processing data collection: Integrate data from the power grid business hall's business processing system, customer service system, and online business platform to collect customer business processing records, such as account opening, account closing, name change and transfer, electricity bill payment, electricity bill inquiry, business consultation, fault reporting, etc., including business type, processing time, processing location, and processing result.

[0040] Customer information data collection: Collect basic information of customers, such as customer name, gender, age, contact information, home address, electricity address, electricity capacity, and credit information such as customer credit rating and historical arrears. Integrate these data with electricity data and business handling data to form a complete customer data portrait.

[0041] Step S11, preprocessing the collected raw data, including cleaning, standardization and missing value processing;

[0042] Specifically, in this step, various preprocessing is as follows:

[0043] Data cleaning: clean the collected raw data, remove invalid data, duplicate data and error data. For example, eliminate data points with abnormally high or low electricity consumption, correct errors in business handling records, and delete duplicate customer records.

[0044] Data standardization: standardize data from different sources and formats to have uniform data format and coding specification. For example, convert electricity consumption time to standard time format, and classify and code business handling types.

[0045] Data missing value processing: for data with missing values, use appropriate methods to fill them. For example, for a small number of missing values in electricity consumption data, mean filling, median filling or time series based prediction filling method can be used; for missing values in customer information data, they can be inferred and filled according to other related information or marked as missing state for special processing in subsequent analysis.

[0046] Step S12, analyze the preprocessed data and predict customer demand according to customer demand prediction model and problem early warning model; the analysis includes electricity consumption trend analysis, electricity consumption mode identification, abnormal electricity consumption detection, business handling frequency analysis, business handling time analysis and business handling correlation analysis;

[0047] Specifically, in this step S12, the specific content of various analysis is as follows:

[0048] Electricity consumption trend analysis: use time series analysis method to analyze customer's historical electricity consumption data and predict customer's future electricity consumption trend. For example, by establishing ARIMA (autoregressive moving average) model or LSTM (long short-term memory) neural network model, analyze the seasonal variation, periodic variation and long-term growth trend of customer's electricity consumption, and predict customer's electricity peak and valley period in advance, providing reference for power generation plan and load dispatching of power enterprises.

[0049] Electricity consumption pattern recognition: using clustering algorithms such as K-Means clustering algorithm, DBSCAN density clustering algorithm, etc. to conduct clustering analysis on customer electricity consumption data, and identify different types of electricity consumption patterns. For example, customers are divided into residential electricity, commercial electricity, industrial electricity, etc. Different types, or further subdivide residential electricity customers into ordinary household electricity, high energy consumption household electricity, etc. Analyze the electricity characteristics and demand differences of different electricity consumption pattern customers to support personalized services.

[0050] Abnormal electricity consumption detection: using data mining algorithms such as Isolation Forest algorithm, Local Outlier Factor algorithm, etc. to detect abnormal situations in customer electricity consumption data. For example, find that the customer's electricity consumption suddenly increases or decreases significantly, or the electricity power fluctuates abnormally, etc. Early warning and troubleshooting, it may be that the customer's home appliance equipment is faulty, electricity stealing behavior or other abnormal situations, to ensure the safe operation of the power grid and the safety of customer electricity consumption.

[0051] Business handling frequency analysis: statistics of customer's different business handling types of handling frequency, analysis of customer's business demand law. For example, some customers often handle electricity fee inquiry business, while other customers frequently handle business consultation or fault repair. According to the business handling frequency, the customers are divided into high-frequency business handling customers, medium-frequency business handling customers and low-frequency business handling customers, and different service strategies are developed for different types of customers.

[0052] Business handling time analysis: analysis of the time distribution characteristics of customer business handling, including the difference of business handling volume in different time periods such as weekdays and weekends, daytime and night. For example, it is found that most customers go to the business hall to handle business after work on weekdays or on weekends, which provides basis for the personnel scheduling and resource allocation of the business hall, and reasonably arranges the staff on duty during the business peak period, improves the service efficiency.

[0053] Business handling association analysis: using association rule mining algorithms such as Apriori algorithm, FP-Growth algorithm, etc. to mine the association relationship between customer business handling behaviors. For example, it is found that customers who handle electricity fee payment business often also query electricity fee details or consult electricity package information at the same time. According to these association relationships, related other businesses can be actively recommended when a customer handles one business, improving the completeness and satisfaction of customer service.

[0054] Among them, the demand prediction model and the problem early warning model need to be established in advance. In specific examples, the two models are established in the following way.

[0055] Demand prediction model: Considering the customer's electricity usage behavior, business handling behavior and external factors (such as seasonal changes, weather conditions, policies and regulations, etc.), a customer demand prediction model is established. For example, using multiple linear regression models, decision tree models or random forest models, etc., to predict the types and times of business that customers may need to handle in the future. For example, according to the increase of customer electricity consumption and seasonal changes, it is predicted that customers may have the demand for electricity package change; according to the aging condition of customer equipment and historical fault repair records, it is predicted that customers may have to repair again, and maintenance personnel and maintenance materials are arranged in advance.

[0056] Problem warning model: Based on the historical data and real-time data of customers, a customer problem warning model is established. For example, by establishing a logistic regression model or a neural network model, it is predicted whether the customer has the risk of arrears, electricity safety hazards and other problems. When the model predicts that the customer may have the risk of arrears, the customer can be reminded in time to pay by sending a short message, making a phone call or pushing through a mobile application; when it is predicted that the customer has electricity safety hazards, the staff can be arranged to check on site and provide safety guidance to avoid accidents.

[0057] Step S13, according to the customer demand prediction results and customer data portrait, personalized service recommendation is provided to guide customers to handle business on the online business platform, actively handle customer problems, and optimize the allocation of business hall resources.

[0058] In step S13, the personalized service recommendation includes pushing the electricity package suitable for the customer's electricity demand, energy-saving product recommendation and electricity safety prompt to the customer through the mobile application, short message or email. In a specific example, according to the customer demand prediction results and customer data portrait, personalized service recommendation is provided for customers. For example, through mobile applications, short messages or emails, etc., customers are pushed with information such as electricity packages suitable for their electricity needs, energy-saving product recommendations, electricity safety tips, etc. For customers predicted to have electricity package change needs, the characteristics and preferential policies of different packages are introduced in detail to guide customers to handle package change business through online channels, reducing the number of visits to the hall to handle business.

[0059] The online service guidance includes: setting up publicity boards and two-dimensional codes in the business hall, guiding customers to download mobile applications and register to use online service functions, and when customers handle business, staff actively introduce the convenience and advantages of online services and guide customers to operate on site. In a specific example, the customer business handling behavior is analyzed, and for businesses suitable for online handling (such as electricity payment, electricity query, business consultation, etc.), customers are guided to use the online service platform to handle business through various channels. For example, publicity boards and two-dimensional codes are set up in the business hall to guide customers to download mobile applications and register to use online service functions; when customers handle business, staff actively introduce the convenience and advantages of online services to customers and guide customers to operate on site; for customers who frequently use the online service platform to handle business, certain points rewards or preferential policies are given to encourage more customers to choose online business handling.

[0060] The active handling of customer problems includes: using a customer problem early warning model to timely discover potential problems existing in customers and communicate with customers to provide solutions or arrange staff to check and repair on site. In a specific example, a customer problem early warning model is used to timely discover potential problems existing in customers and actively handle them. For customers at risk of arrears, communicate with customers in advance to understand their payment difficulties and provide corresponding solutions such as installment payment and delayed payment; for customers with potential electrical safety hazards, timely arrange staff to check and repair on site and provide customers with safety knowledge training and propaganda materials to improve their safety awareness. By actively handling customer problems, the number of customers who come to the hall for consultation or complaints due to problems is reduced.

[0061] The optimal allocation of business hall resources includes: reasonably arranging the staff scheduling and window settings of the business hall according to the customer business handling time analysis results, and optimizing the functional layout of the business hall. In a specific example, the staff scheduling and window settings of the business hall are reasonably arranged according to the customer business handling time analysis results. During the business peak period, the number of staff is increased and more business handling windows are opened to improve the business handling speed and reduce the customer waiting time; during the business valley period, the number of staff is appropriately reduced and some staff are arranged to train, arrange business or other back-end work to improve the efficiency of human resources. At the same time, according to the distribution of customer business handling types, the functional layout of the business hall is optimized, and the frequently used business handling area is set in a conspicuous position to facilitate customers to quickly find and handle business.

[0062] It can be understood that the present application predicts the business that customers may need to handle by analyzing customer electricity usage behavior, business handling records and other data, actively pushes personalized messages, timely business handling period reminders and electricity safety and equipment maintenance warnings. The customer satisfaction is improved, and the business hall working time and queuing situation are reduced.

[0063] As Figure 2 shown, a structural schematic diagram of an embodiment of the power grid business hall intelligent service optimization system provided by the application is shown. In combination with Figure 3 shown, in this embodiment, the power grid business hall intelligent service optimization system 1 comprises:

[0064] a data collection and integration module 10, configured to collect power consumption data, service handling data and customer information data of customers, and integrate the data to form a customer data portrait;

[0065] a data preprocessing module 11, configured to clean, standardize and process missing values of the collected original data;

[0066] a big data analysis module 12, configured to analyze the preprocessed data, and predict customer demand according to a customer demand prediction model and a problem early warning model; the analysis includes power consumption trend analysis, power consumption mode identification, abnormal power consumption detection, service handling frequency analysis, service handling time analysis and service handling correlation analysis;

[0067] a service optimization processing module 13, configured to provide personalized service recommendation according to the customer demand prediction result and the customer data portrait, guide customers to handle services using an online service platform, actively handle customer problems, and optimize business hall resource allocation.

[0068] In a specific example, as Figure 3 shown, the service optimization processing module 13 further comprises:

[0069] a personalized service recommendation unit 130, configured to push power tariff packages suitable for the power consumption demand of the customers, energy-saving product recommendations and power consumption safety tips to the customers through a mobile application, short message or email;

[0070] an online service guidance unit 131, configured to set up a propaganda board and a two-dimensional code in the business hall, guide customers to download a mobile application and register to use online service functions, and actively introduce the convenience and advantages of online services to customers when they handle services, and guide customers to operate on site;

[0071] a customer problem active handling unit 132, configured to use a customer problem early warning model to timely find potential problems existing in the customers, and communicate with the customers to provide solutions, or arrange staff to check and repair on site;

[0072] a business hall resource optimized allocation unit 133, configured to reasonably arrange personnel scheduling and window settings of the business hall according to the customer service handling time analysis result, and optimize the functional layout of the business hall.

[0073] For more details, please refer to the foregoingFigure 1 The description of the above-mentioned embodiments of the present application is not redundant here.

[0074] The embodiments of the present application have the following beneficial effects:

[0075] The present application provides a power grid business hall intelligent service optimization method and system, which can accurately predict customer demand and potential problems by using big data analysis technology, so that the service is more forward-looking and targeted. By actively pushing personalized service recommendations and online business guidance, the number of visits to the hall and the waiting time of customers are effectively reduced, and the service efficiency is improved. Customers can conveniently handle business through online channels and enjoy more efficient and convenient service experience, thereby improving customer satisfaction.

[0076] According to the analysis result of the customer business handling time, the present application reasonably arranges the personnel scheduling and window setting of the business hall, so that the resources of the business hall are more reasonably utilized. By optimizing the function layout, the operation and management level of the business hall is improved, so that the business hall can operate more efficiently and orderly. The operation cost is effectively reduced, and the valuable resources of the power grid enterprise are saved.

[0077] The present application enhances the customer's dependence and stickiness to the power grid business hall by providing personalized services and online business guidance, so that customers are more willing to choose the power grid business hall as their service provider. The implementation of the present application improves the competitiveness of the power grid enterprise in the power market, so that the power grid enterprise can attract more customers and expand the business market share.

[0078] The present application can timely find the power utilization safety hazards such as equipment aging and overload by analyzing the customer's power utilization equipment data, so as to remind the customer to maintain or repair the equipment. By improving the customer's safety utilization awareness, the present application helps to reduce safety accidents caused by improper power utilization and ensures the safe operation of the power grid.

[0079] In summary, the present application improves the service quality and operation efficiency, enhances the customer stickiness and market competitiveness. At the same time, the present application also improves the customer's safety utilization awareness, and lays a solid foundation for the sustainable development of the power grid enterprise.

[0080] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, units or computer program products. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt 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.

[0081] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart blocks and / or blocks in the flowcharts and / or block diagrams can represent a Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0082] The above-described embodiments are merely given as non-limiting examples. It will be understood by those skilled in the art that various modifications, changes, additions and omissions can be made without departing from the spirit and essential characteristics of the application. Therefore, the scope of the present application should be determined not by the foregoing description but by the appended claims.

Claims

1. A method for optimizing intelligent services in power grid business halls, characterized in that, Includes the following steps: Collect customer electricity consumption data, business processing data, and customer information data, and integrate the data to form a customer data profile; The collected raw data is preprocessed, including cleaning, standardization, and handling of missing values. The preprocessed data is analyzed, and customer demand is predicted based on the customer demand prediction model and the problem early warning model. The analysis includes electricity consumption trend analysis, electricity consumption pattern recognition, abnormal electricity consumption detection, business processing frequency analysis, business processing time analysis, and business processing correlation analysis. Based on customer demand forecasts and customer data profiles, we provide personalized service recommendations, guide customers to use online business platforms to conduct business, proactively address customer issues, and optimize the allocation of resources in business halls.

2. The intelligent service optimization method for power grid business halls according to claim 1, characterized in that, The personalized service recommendations include pushing electricity packages suitable for customers' electricity needs, energy-saving product recommendations, and electricity safety tips to customers via mobile applications, SMS, or email.

3. The method for optimizing intelligent services in power grid business halls according to claim 2, characterized in that, The online service guidance includes: setting up promotional display boards and QR codes in the business hall to guide customers to download the mobile application and register to use the online service functions; and when customers are handling business, staff will proactively introduce the convenience and advantages of online services and provide on-site guidance to customers on how to operate them.

4. The intelligent service optimization method for power grid business halls according to claim 3, characterized in that, The proactive handling of customer issues includes: using a customer issue early warning model to promptly identify potential problems with customers, communicating with customers to provide solutions, or arranging for staff to conduct on-site inspections and repairs.

5. The intelligent service optimization method for power grid business halls according to claim 4, characterized in that, The optimized allocation of business hall resources includes: rationally arranging staff shifts and window settings in the business hall based on the analysis results of customer business processing time, as well as optimizing the functional layout of the business hall.

6. A smart service optimization system for power grid business halls, characterized in that, include: The data acquisition and integration module is used to collect customer electricity consumption data, business processing data, and customer information data, and integrate the data to form a customer data profile. The data preprocessing module is used to preprocess the collected raw data. The preprocessing includes cleaning, standardization, and handling of missing values. The big data analytics module is used to analyze the preprocessed data and predict customer demand based on customer demand prediction models and problem early warning models. The analysis includes electricity consumption trend analysis, electricity consumption pattern recognition, abnormal electricity consumption detection, business processing frequency analysis, business processing time analysis, and business processing correlation analysis. The service optimization module is used to provide personalized service recommendations based on customer demand predictions and customer data profiles, guide customers to use the online business platform to handle business, proactively handle customer issues, and optimize the allocation of resources in the business hall.

7. The intelligent service optimization system for power grid business halls according to claim 6, characterized in that, The service optimization processing module further includes: The personalized service recommendation unit is used to push electricity packages, energy-saving product recommendations, and electricity safety tips to customers via mobile application, SMS, or email, which are suitable for their electricity needs. The online business guidance unit is used to set up promotional display boards and QR codes in the business hall to guide customers to download the mobile application and register to use the online business functions. When customers are handling business, staff will proactively introduce the convenience and advantages of online business and provide on-site guidance to customers on how to operate it. The proactive customer issue handling unit is used to promptly identify potential problems with customers using a customer issue early warning model, communicate with customers to provide solutions, or arrange for staff to conduct on-site inspections and repairs. The business hall resource optimization and allocation unit is used to rationally arrange staff scheduling and window settings in the business hall based on the analysis results of customer business processing time, as well as optimize the functional layout of the business hall.

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