Client appeal pre-intervention method based on data analysis

Abstract

The invention provides a customer demand pre-intervention method based on data analysis, and the method comprises the steps: carrying out the collection and fusion of multi-source data, and integrating the data of a customer, a power grid and construction; a customer power connection appeal prediction model is constructed, and a machine learning algorithm is used to accurately predict appeal probability and power connection time; then, the construction bearing capacity and resource availability are evaluated, and accurate resource allocation is achieved; then, customer appeal pre-response and process transparent pushing are carried out, so that the service experience is improved; and finally, continuously optimizing the model and the service process through closed-loop feedback. According to the method, advanced perception of customer demands, accurate resource allocation and service closed-loop management and control are realized, the problems of difficult scheduling of the generator car, uncertain power connection time and opaque service are solved, the resource utilization efficiency is improved, the customer waiting time is shortened, and the customer satisfaction and the enterprise economic benefit are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system distribution network customer service, in particular to a customer demand pre-intervention method based on data analysis. BACKGROUND

[0002] In the field of distribution network industry expansion service, with the rapid development of social economy and the continuous growth of power demand, customers have higher requirements for the quality and efficiency of power supply service. However, the existing distribution network industry expansion service technology and method has many shortcomings, which is difficult to meet the increasingly diversified and personalized needs of customers, which is specifically manifested in the following aspects: The existing distribution network industry expansion service involves multiple departments and systems, and the data sources are extensive and diverse. Customer basic information, power grid operation data, construction progress data, and historical power supply data are scattered in different systems, and lack effective integration and management. This leads to difficulty in ensuring the consistency and accuracy of data, and there may be differences between different departments, which brings great difficulty to subsequent analysis and decision-making.

[0003] Due to the dispersion of data, each department often needs to spend a lot of time and effort to collect and organize data when conducting business analysis and decision-making, and different departments may repeat the collection of the same data, causing waste of resources. At the same time, due to the lack of a unified data warehouse, data query and use are not convenient enough, affecting work efficiency.

[0004] Currently, when predicting customer power supply demands, mainly rely on artificial experience and simple statistical analysis method, lack of scientific, accurate prediction model. This way is difficult to fully consider the diversity and uncertainty of customer demand, cannot accurately predict the occurrence probability and predicted power supply time of customer power supply demand, leading to the enterprise in dealing with customer power supply demand often in a passive state, difficult to do preparation work in advance.

[0005] Due to the inability to accurately predict customer power supply time, the enterprise lacks pertinence when arranging construction and resource allocation, which may lead to unreasonable allocation of construction teams and material resources, resulting in long waiting time for customers and reducing customer satisfaction.

[0006] In the evaluation of the construction carrying capacity of the construction team, the existing method often only considers the simple factors of the number of construction personnel and equipment, without fully considering the skill level, work experience of construction personnel and performance factors of construction equipment. This leads to inaccurate assessment of the construction capacity of the construction team, and cannot reasonably arrange construction tasks, which may cause construction progress delay.

[0007] For the scheduling of material resources and non-material resources, the existing method lacks effective coordination and optimization mechanism. In particular, the scheduling of power generation car resources often appears idle or not timely due to the lack of accurate grasp of the use of power generation car and customer demand, which affects the normal development of power distribution network expansion service.

[0008] In the existing power distribution network expansion service, customers often have little knowledge of the service process and progress, and can only passively wait for the completion of the service. The enterprise has not established an effective information communication mechanism, and cannot timely push the power connection progress, construction arrangement, and resource allocation information to the customers, resulting in the lack of trust of the customers in the service process and the easy generation of dissatisfaction.

[0009] The existing service mode is mainly that the enterprise responds and handles after the customer puts forward the demand, which belongs to the passive service mode. This mode cannot timely discover and solve the potential problems of the customers, and cannot meet the demand of the customers for active and efficient service.

[0010] The existing power distribution network expansion service lacks effective closed-loop feedback mechanism, cannot timely collect the actual power connection situation and feedback information of the customers, and cannot compare and analyze these information with the prediction results. This leads to the difficulty of the enterprise in discovering the problems and deficiencies in the service process, and the inability of the enterprise to timely adjust and optimize the service process and prediction model, so that the service quality is difficult to be continuously improved.

[0011] Due to the lack of data support and scientific analysis method, the enterprise often lacks pertinence and effectiveness when improving the service process, construction bearing capacity evaluation method, and resource allocation strategy, and it is difficult to realize continuous optimization and adapt to the changing market demand and customer requirements.

[0012] The existing power distribution network expansion service technology and method have many problems, and an customer demand pre-intervention method based on data analysis is urgently needed to solve the problems of power generation car resource scheduling difficulty, uncertain customer power connection time, and non-transparent service process, realize the early perception of customer demand, accurate allocation of resources, and closed-loop control of service process. Therefore, a customer demand pre-intervention method based on data analysis is proposed. SUMMARY

[0013] The purpose of the present application is to solve the problems of the background art. In order to achieve the above-mentioned purpose of the application, the present application provides the following technical solution: a customer demand pre-intervention method based on data analysis, comprising the following steps: step 1, multi-source data acquisition and fusion, acquiring multi-source heterogeneous data through enterprise resource planning system (ERP), distribution automation system (DMS), electricity information acquisition system (AMI), project management system (PMS), and customer service system (CRM), the data including: customer installation capacity , application time , electrical properties , ; distribution network equipment load rate , number of remaining intervals of substation , line accessible capacity ; number of projects under construction , current construction task amount , material arrival cycle , and weather temperature and regional development planning label ; After cleaning, deduplication, and standardization of the above data, a unified data center is constructed, and a four-dimensional association graph of customers, power grids, projects, and resources is established , wherein the node set represents an entity object, and the edge set represents a business or spatial relationship Step 2, customer power connection appeal prediction model construction, based on historical work order data set , wherein the input feature vector includes customer type, load level, surrounding project density, and approval cycle, and the output label indicates whether there is a delay in power connection, and a binary classification machine learning model is trained for predicting the power connection appeal urgency level of customers in each grid area within the next days; a time series model ARIMA is used to predict the trend of regional daily average installation quantity; DBSCAN clustering algorithm is used to identify spatial hotspots to generate a high-priority customer list ; Step 3, construction carrying capacity and resource assessment: calculate the task saturation of the current construction team , and set a threshold value, such as 0.85, to determine if it is overloaded; use the power flow calculation equation to analyze the voltage deviation and thermal stability margin of the target feeder to determine if the new load connection condition is met; and according to the dispatching distance function of the generator car and the available window period, evaluate the feasibility of temporary power supply; finally output the regional carrying capacity thermal map and resource gap list ; Step 4, customer appeal pre-response and process transparency push: when the prediction result and resource assessment show that there is a service bottleneck, trigger the pre-response mechanism: automatically push the warning to the production command center and start the resource coordination process; for the predicted power connection delay For customers of the system, the system generates personalized communication scripts and recommends alternatives, and pushes service dashboards including work order stages, estimated completion time, responsible departments, and changes in real time through APP, SMS, or WeChat public number; manual suspension of work orders is prohibited, and all abnormal operations must be approved and leave traces; Step 5, closed-loop feedback and continuous optimization mechanism, collect customer satisfaction scores , actual power-on time , prediction error and complaint records, build a feedback database ; calculate key performance indicators KPI every month: And the deviation sample is returned to the training set to update the prediction model parameters, forming a perception, decision, execution, and feedback closed-loop optimization mechanism.

[0014] As a preferred technical solution of the application, the customer power-on appeal prediction model uses an ensemble learning algorithm , and the objective function is defined as: Where is the loss function, including logistic loss, is the number of leaf nodes, is the leaf weight, is the regularization parameter; the model is incrementally trained every 7 days to ensure adaptation to dynamic business changes.

[0015] As a preferred technical solution of the application, the spatial clustering analysis uses an improved algorithm with a neighborhood radius , minimum point number , and weighted density: High-capacity users are used to identify real power demand hotspots.

[0016] As a preferred technical solution of the application, the construction load capacity evaluation also includes material support capability analysis: if the inventory of a key device project duration , mark the area as having material constraint risks and automatically trigger an emergency procurement process.

[0017] As a preferred technical solution of the application, the power generation vehicle dispatching feasibility determination logic is as follows: for a customer to be connected to power and available power generation vehicles , if: If the temporary power supply capability is possessed, the system automatically generates an emergency power supply scheme and pushes it to the on-site person in charge.

[0018] As a preferred technical solution of the application, in the pre-response mechanism, the predicted power-on time The calculation formula is: The duration of each stage is dynamically adjusted based on historical average values and current resource status, and when any link is delayed, the customer is automatically notified of the re-estimation.

[0019] As a preferred technical solution of the application, in the customer power-on appeal prediction model construction step, the key features are sorted by feature importance, and the random forest algorithm is used to calculate the feature importance score to remove features with importance scores below a certain threshold.

[0020] As a preferred technical solution of the application, in the construction bearing capacity and resource assessment step, the inventory management of material resources uses the ABC classification method, and different management strategies are used for different categories of materials.

[0021] As a preferred technical solution of the application, in the customer appeal pre-response and process transparency push step, sentiment analysis is performed on customer feedback information, and a combination of sentiment dictionary matching and machine learning classification is used to determine the sentiment tendency of customer feedback.

[0022] As a preferred technical solution of the application, it is suitable for urban distribution network industry expansion, temporary power supply approval, industrial park centralized power supply, major event power supply and intelligent operation of district production command center scenarios.

[0023] Compared with the prior art, the beneficial effects of the present application are: through the multi-source data acquisition and fusion step, the data related to the expansion of the distribution network service are collected, including customer basic information, power grid operation data, construction progress data, historical power-on data, and are cleaned, converted and fused. This not only ensures the comprehensiveness of the data, can reflect the actual situation of the expansion of the distribution network service from multiple dimensions, but also removes noise and error information, integrates data of different formats and sources into a data warehouse, greatly improves the accuracy of the data, and provides a reliable data foundation for subsequent analysis and decision-making.

[0024] The unified data warehouse makes the storage and management of data more orderly, facilitating subsequent queries and use. Different departments and personnel can obtain the required data based on this data warehouse, avoiding the problem of scattered and repeated collection of data, improving the utilization efficiency of data, and reducing the decision-making errors caused by inconsistent data.

[0025] Based on the fused multi-source data, a customer power connection demand prediction model is constructed, key features related to customer power connection demand are selected, and neural network, decision tree, and support vector machine machine learning algorithms are used for modeling. This model can accurately predict the probability of customer power connection demand and the expected power connection time, allowing the enterprise to anticipate customer needs and make appropriate preparations, avoiding service lag caused by sudden customer demand.

[0026] By accurately predicting customer power connection time, the enterprise can reasonably arrange construction and resource allocation, reduce customer waiting time, and improve customer satisfaction. In the construction carrying capacity and resource assessment step, the number of construction team members, skill level, work experience, and the number of construction equipment, performance factors are considered to accurately assess the construction carrying capacity of the construction team. At the same time, the availability of material resources and non-material resources is analyzed. According to the output results of the customer power connection demand prediction model, resource allocation plans are developed to achieve precise resource allocation and avoid waste and idling of resources.

[0027] To solve the problem of difficult scheduling of power generation car resources in existing distribution network expansion services, this method assesses and precisely allocates the availability of power generation car resources, allowing for reasonable arrangement of power generation car use according to customer demand, improving the efficiency of power generation car use, and ensuring timely power generation support for customers when needed.

[0028] In the customer demand pre-response and process transparency push step, customers are pushed real-time information on power connection progress, construction arrangement, and resource allocation through SMS and WeChat public number channels, allowing customers to clearly understand the entire service process and enhancing customer trust in the service. At the same time, a customer feedback mechanism is established to collect customer opinions and suggestions, quickly respond to and handle customer problems and complaints, and improve customer satisfaction.

[0029] According to the prediction results of the customer power connection demand prediction model, customers who may have power connection problems are identified in advance, and personalized solutions are provided to proactively communicate with customers. This service forward and proactive intervention approach changes the traditional passive service mode, allowing for measures to be taken to solve problems before they occur, improving service quality and efficiency.

[0030] In the closed-loop feedback and continuous optimization mechanism step, the actual power connection situation and feedback information of customers are collected and compared with the prediction results of the customer power connection demand prediction model to evaluate the accuracy and effectiveness of the model. Based on the comparison results, the customer power connection demand prediction model is adjusted and optimized, and the parameters and structure of the model are updated to make the prediction results more accurate and improve the enterprise's prediction ability of customer demand.

[0031] Analyze the problems and deficiencies in the service process, continuously improve and optimize the construction bearing capacity evaluation method, resource allocation strategy and service process. Through continuous improvement and perfection, the service level of the enterprise is continuously improved, which can better meet the needs of customers and enhance the market competitiveness of the enterprise.

[0032] The method realizes the early perception of customer demands, accurate allocation of resources and closed-loop management and control of service processes. From data collection, customer demand prediction, resource allocation, service provision to feedback optimization, a complete closed loop is formed, each link is interrelated and interdependent, which can timely discover and solve problems in the business process, and improve the overall operation efficiency and management level of the enterprise.

[0033] Through improving resource utilization efficiency, reducing customer waiting time and improving customer satisfaction, the method can bring significant economic benefits to the enterprise. On the one hand, it reduces the waste and idling of resources and reduces the operating cost; on the other hand, it improves the customer loyalty and reputation, which helps to expand market share and increase the income of the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The method steps provided by the present application are intended to represent; Figure 2 The multi-source data collection and fusion table provided by the present application is provided; Figure 3 The customer demand prediction model construction table provided by the present application is provided; Figure 4 The construction bearing capacity and resource availability evaluation table provided by the present application is provided; Figure 5 The customer demand pre-response and process transparency push table provided by the present application is provided; Figure 6 The closed-loop feedback and continuous optimization mechanism table provided by the present application is provided. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are one specific embodiment of the present application, and are not limited to all embodiments.

[0036] Therefore, the following detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but only represents some embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0037] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict, and it should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0038] Embodiment 1: A customer appeal pre-intervention method based on data analysis, comprising the following steps: Step 1, multi-source data acquisition and fusion, through enterprise resource planning system (ERP), distribution automation system (DMS), electricity information acquisition system (AMI), project management system (PMS) and customer service system (CRM), multi-source heterogeneous data is collected, including: customer installation capacity , application time , electricity property , , distribution network equipment load rate , remaining interval number of substation , line accessible capacity , number of construction projects under construction , current construction task quantity , material arrival cycle , and weather temperature and regional development planning label ; After cleaning, deduplication and standardization of the above data, a unified data center is constructed, and a four-dimensional association graph of customers, power grids, projects and resources is established , wherein the node set represents an entity object, and the edge set represents a business or spatial relationship. Step 2, customer power connection appeal prediction model construction, based on historical work order data set , wherein the input feature vector includes customer type, load level, surrounding engineering density, approval cycle, output label indicates whether there is a delay in power connection, and a binary classification machine learning model is trained for predicting the power connection appeal urgency level of customers in each grid area within the next days; at the same time, the time series model ARIMA is used to predict the regional daily installation quantity trend; combined with the DBSCAN clustering algorithm, the spatial hot area is identified to generate a high-priority customer list . Step 3, construction carrying capacity and resource assessment: calculate the task saturation of the current construction team , and set a threshold value, such as 0.85, to determine overload; use the power flow calculation equation: Analyze the voltage deviation and thermal stability margin of the target feeder to determine whether the conditions for new load access are met; and based on the generator dispatch distance function... And the available window period, assess the feasibility of temporary power supply; finally output the regional load-bearing capacity heat map. Resource Gap List ; Step 4: Proactive Response to Customer Needs and Transparent Process Push. When forecast results and resource assessments indicate service bottlenecks, a proactive response mechanism is triggered: an alert is automatically pushed to the production command center, initiating a resource coordination process; for expected power connection delays exceeding [a certain threshold], [further action is taken]. For customers, the system generates personalized communication scripts and recommends alternative solutions, and pushes service dashboards containing work order stages, estimated completion times, responsible departments, and reasons for changes in real time via APP, SMS, or WeChat official account; manual cancellation of work orders is prohibited, and all abnormal operations must be approved and recorded. Step 5: Establish a closed-loop feedback and continuous optimization mechanism to collect customer satisfaction scores. Actual power connection time Prediction error And record complaints and incidents to build a feedback database. ; Calculate Key Performance Indicators (KPIs) monthly: The biased samples are then fed back into the training set to update the prediction model parameters, forming a closed-loop optimization mechanism encompassing perception, decision-making, execution, and feedback.

[0039] Customer call demand prediction model Employing ensemble learning algorithms Its objective function is defined as: in Loss functions include logistic loss. The number of leaf nodes. For leaf weight, These are regularization parameters; the model is incrementally trained every 7 days to ensure it adapts to dynamic business changes.

[0040] Spatial clustering analysis using an improved method Algorithm, its neighborhood radius , the minimum number of points And introduce weighted density: Identify the real hotspots of electricity demand by focusing on high-capacity users.

[0041] The construction bearing capacity evaluation also includes material support capability analysis: if the inventory of a certain key equipment The project duration, mark the area exists material constraint risk, and automatically trigger the emergency procurement process.

[0042] The dispatching feasibility of the power generation vehicle is determined as follows: for the customer to be connected And the available power generation vehicle , if it satisfies: It is considered to have temporary power supply capacity, and the system automatically generates an emergency power supply scheme and pushes it to the site manager.

[0043] In the pre-response mechanism, the calculation formula of the estimated connection time is: The duration of each stage is dynamically adjusted based on historical average values and current resource status, and when any link is delayed, it is automatically re-estimated and the customer is notified.

[0044] In the construction bearing capacity and resource evaluation step, the inventory management of material resources uses the ABC classification method, and different management strategies are used for different categories of materials.

[0045] In the construction bearing capacity and resource evaluation step, the inventory management of material resources uses the ABC classification method, and different management strategies are used for different categories of materials.

[0046] In the customer demand pre-response and process transparency push step, sentiment analysis is performed on customer feedback information, and a combination of sentiment dictionary matching and machine learning classification is used to determine the sentiment tendency of customer feedback. It is suitable for urban distribution network industry expansion, temporary power supply approval, industrial park centralized power supply, major event power supply, and intelligent operation of district production command center scenarios.

[0047] The working principle of the customer demand pre-intervention method based on data analysis: collect multi-source data related to distribution network industry expansion services, including customer basic information such as customer type, power demand, power grid operation data such as voltage, current, and load, construction progress data such as construction stage, completion time, and historical connection data such as past connection time and connection problems. These data come from different systems and channels, providing a comprehensive and rich information base for subsequent analysis and modeling.

[0048] The collected multi-source data is cleaned, converted and fused. The cleaning process removes noise and error information in the data, removes duplicate records, and corrects incorrect timestamps to ensure data accuracy and consistency. The conversion operation unifies data of different formats and sources, and unifies date formats in different systems. Finally, the processed data is integrated into a data warehouse for subsequent unified management and analysis.

[0049] Based on the fused multi-source data, key features related to customer power connection appeals are screened out. These features may include customer's power demand, current load of power grid, construction progress. By selecting key features, the data dimension can be reduced, and the training efficiency and prediction accuracy of the model can be improved.

[0050] Machine learning algorithms, including neural networks, decision trees, and support vector machines, are used to build a customer power connection appeal prediction model. The model takes the selected key features as input, and through the learning and calculation of the algorithm, outputs the probability of customer power connection appeal and the predicted power connection time. Different machine learning algorithms have different characteristics and application scenarios, and by selecting the appropriate algorithm, the performance of the model can be improved.

[0051] Model training and optimization use historical data to train the constructed prediction model, and by continuously adjusting the parameters and structure of the model, the model can better fit the historical data. During the training process, according to the difference between the model's prediction results and the actual results, optimization algorithms, including gradient descent method, are used to update the model's parameters, in order to improve the model's accuracy and generalization ability. The construction team's construction personnel number, skill level, work experience, and construction equipment number, performance factors are considered to evaluate the construction team's workload that can be undertaken within a certain period of time. According to the skill level and work experience of the construction personnel, the amount of work they can complete per day is determined, and then combined with the number and performance of the construction equipment, the overall construction capacity of the construction team is calculated.

[0052] The availability of material resources, including power generation vehicles, cables, and transformers, and non-material resources, including construction sites and power outage plans, is evaluated. Analyze the inventory quantity, distribution location, and usage status information of the resources to determine whether the resources can meet the customer's power connection demand. Check the inventory quantity and distribution location of the power generation vehicles, and whether there are vehicles being repaired or in use. According to the output results of the customer power connection appeal prediction model and the construction carrying capacity and resource availability evaluation results, a reasonable resource allocation plan is developed. If it is predicted that the customer power connection demand in a certain area is large, and the local resources are insufficient, resources can be allocated from other areas to ensure that customers can be connected to power on time.

[0053] According to the prediction results of the customer power connection appeal prediction model, customers who may have power connection problems are identified in advance. These customers are actively communicated with to understand their needs and concerns and provide personalized solutions. If it is predicted that a customer's power connection time may be delayed, the customer is communicated with in a timely manner to explain the reasons and provide alternative solutions.

[0054] Through the channels of SMS and WeChat public number, customers are pushed with real-time power connection progress, construction arrangement, and resource allocation information. Customers can timely understand the progress of the service, and the transparency of the service and the satisfaction of the customers are improved. SMS is sent to customers at regular intervals every day to inform them of the work completed today and the construction plan for tomorrow.

[0055] A customer feedback mechanism is established to collect customers' opinions and suggestions. For the problems and complaints raised by customers, quick response and handling are provided to timely solve customers' problems and improve customers' satisfaction. A dedicated customer service hotline or online customer service platform is set up to handle customers' feedback in a timely manner.

[0056] The actual power connection situation and feedback information of customers are collected and compared with the prediction results of the customer power connection appeal prediction model. The accuracy and effectiveness of the model are evaluated, the error between the predicted power connection time and the actual power connection time, and the prediction accuracy of the probability of power connection appeal are calculated. According to the comparison and analysis results, the customer power connection appeal prediction model is adjusted and optimized. The parameters and structure of the model are updated to improve the prediction accuracy and adaptability of the model. If it is found that the prediction error of the model is large in some cases, relevant features can be added or the algorithm of the model can be adjusted.

[0057] Problems and deficiencies in the service process are analyzed, and the construction bearing capacity assessment method, resource allocation strategy, and service process are continuously improved and optimized. If it is found that there are low-efficiency problems in the resource allocation process, the resource allocation process and algorithm can be optimized to improve the utilization efficiency of resources.

[0058] The above examples are only used to illustrate the present application and do not limit the technical solutions described in the present application. Although the present application has been described in detail with reference to the above embodiments, the present application is not limited to the above specific embodiments, and therefore any modification or substitution of the present application; all technical solutions and improvements that do not deviate from the spirit and scope of the application are covered in the scope of the claims of the present application.

Claims

1. A method for proactive intervention in customer needs based on data analysis, characterized in that, Includes the following steps: Step 1: Multi-source data acquisition and fusion. Multi-source heterogeneous data is collected through Enterprise Resource Planning (ERP), Distribution Automation System (DMS), Electricity Information Collection System (AMI), Project Management System (PMS), and Customer Relationship Management (CRM). This data includes: customer-reported installation capacity. Application period Electrical properties , ; Load rate of power distribution network equipment Number of remaining bays in substations Line access capacity Number of projects under construction Current construction workload Material delivery cycle and weather temperature and regional development planning tags ; After cleaning, deduplication, and standardization of the above data, a unified data platform is constructed, and a four-dimensional relationship graph of customers, power grid, engineering, and resources is established. , where the node set Represents entity objects, edge sets Indicates business or spatial relationships; Step 2: Building a customer call request prediction model based on historical work order datasets. , where the input feature vector Includes customer type, load level, surrounding project density, approval cycle, and output tags. Indicate whether a delayed power connection has occurred, and train a binary classification machine learning model. Used to predict the future The urgency level of customers' power connection requests within each grid area within a given day; simultaneously employing a time series model. ARIMA-based prediction of regional daily average installation application trends; combined with DBSCAN clustering algorithm to identify spatial hotspots and generate a high-priority customer list. ; Step 3, Construction Bearing Capacity and Resource Assessment: Calculate the current construction team's load-bearing capacity and resources. Task saturation A preset threshold of 0.85 is considered overload; the calculation equation is as follows: Analyze the voltage deviation and thermal stability margin of the target feeder to determine whether the conditions for new load access are met; and based on the generator dispatch distance function... During the window period, assess temporary power supply; finally, output a heat map of the area's load-bearing capacity. Resource Gap List ; Step 4: Proactive Response to Customer Needs and Transparent Process Push. When forecast results and resource assessments indicate service bottlenecks, a proactive response mechanism is triggered: an alert is automatically pushed to the production command center, initiating a resource coordination process; for expected power connection delays exceeding [a certain threshold], [further action is taken]. For customers, the system generates personalized communication scripts and recommends alternative solutions, and pushes service dashboards containing work order stages, estimated completion times, responsible departments, and reasons for changes in real time via APP, SMS, or WeChat official account; manual cancellation of work orders is prohibited, and all abnormal operations must be approved and recorded. Step 5: Establish a closed-loop feedback and continuous optimization mechanism to collect customer satisfaction scores. Actual power connection time Prediction error And record complaints and incidents to build a feedback database. ; Calculate Key Performance Indicators (KPIs) monthly: The biased samples are then fed back into the training set to update the prediction model parameters, forming a closed-loop optimization mechanism encompassing perception, decision-making, execution, and feedback.

2. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, The customer call demand prediction model Employing ensemble learning algorithms Its objective function is defined as: in Loss functions include logistic loss. The number of leaf nodes. For leaf weight, For regularization parameters; The model is incrementally trained every 7 days to ensure it adapts to dynamic business changes.

3. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, The spatial clustering analysis employs an improved method. Algorithm, its neighborhood radius , the minimum number of points And introduce weighted density: Identify the real hotspots of electricity demand by focusing on high-capacity users.

4. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, The construction bearing capacity assessment also includes an analysis of material support capabilities: if the inventory of a certain key equipment... The project schedule is monitored, and the area is marked as having risks, which automatically triggers an emergency procurement process.

5. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, The generator dispatching decision logic is as follows: for customers awaiting power connection... and standby generator car If the following conditions are met: If the system determines that it has temporary power supply capability, it will automatically generate an emergency power supply plan and push it to the person in charge on site.

6. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, In the aforementioned pre-response mechanism, the estimated power connection time is... The calculation formula is: The duration of each stage is dynamically adjusted based on historical averages and current resource status, and the system automatically re-estimates and notifies the customer if any stage is delayed.

7. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, In the customer call request prediction model construction step, key features are ranked by importance, a random forest algorithm is used to calculate feature importance scores, and features with importance scores below a set threshold are removed.

8. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, In the construction bearing capacity and resource assessment steps, the inventory management of material resources adopts the ABC classification method, and different management strategies are used for different categories of materials.

9. The method for proactive intervention in customer needs based on data analysis according to claim 1, characterized in that, In the steps of proactively responding to customer requests and making processes transparent, sentiment analysis is performed on customer feedback information, and a combination of sentiment dictionary matching and machine learning classification is used to determine the sentiment tendency of customer feedback.

10. A method for proactive intervention in customer needs based on data analysis according to claim 9, characterized in that, It is applicable to scenarios such as urban power distribution network expansion applications, temporary power supply approval, centralized power supply in industrial parks, power supply for major events, and intelligent operation of district bureau production command centers.

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