Offline multi-channel customer demand processing system based on grid control

Abstract

The invention discloses an offline multi-channel customer demand processing system based on grid control. According to the system, a layered system and a multi-module cooperation mode are adopted, WeChat, phone and other multi-channel customer appeal data are collected through the data collection layer, and after cleaning and fusion are conducted through the data processing layer, the functions of work order control, service quality analysis and the like are achieved through the service logic layer. The system utilizes a plurality of advanced algorithms, such as natural language processing, voice recognition, intelligent work order distribution and other technologies, improves the appeal processing efficiency and quality, constructs a service quality evaluation model, and carries out risk prediction and intelligent decision making. In the aspect of performance, a cache and distributed architecture is adopted to guarantee the response speed and the concurrent processing capability, and good expansibility and compatibility are achieved. In addition, data security and privacy protection are ensured through an AES encryption algorithm and a block chain technology, and an algorithm model is optimized regularly. The system effectively solves the problem of a traditional service mode, and improves the service level and customer satisfaction of a power enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of power customer service control, and particularly relates to an offline multi-channel customer demand processing system based on grid control. Background Art

[0002] With the rapid development of the power industry and the increasing diversification of customer needs, the traditional customer service mode has been difficult to meet the needs of modern power users. Especially in the processing of customer demands, the existing service channels are scattered and the phenomenon of information islands is serious, resulting in low efficiency in processing customer demands and difficult to guarantee service quality. Power enterprises urgently need to build a set of efficient and intelligent customer service process processing systems to achieve unified control and rapid response to multi-channel customer demands, improve customer satisfaction, and optimize the power business environment.

[0003] Currently, power enterprises mainly receive customer demands through online channels and offline channels. However, there are problems of information non-communication and data non-sharing between these channels, resulting in complex customer demand processing processes, slow response speeds, and difficult to effectively monitor and evaluate service quality. The existing customer service systems still have large technical bottlenecks in data processing, risk prediction, work order allocation, etc., and cannot meet the growing customer needs and complex business scenarios. The specific manifestations are as follows: Difficulty in multi-channel data fusion: The data formats of channels such as WeChat, telephone, and landline are different, making it difficult to achieve in-depth data fusion and unified control, resulting in low efficiency in processing customer demands. Low speech recognition accuracy: The existing speech recognition technology has low recognition accuracy in complex environments (such as noise interference, dialects, etc.), and it is difficult to meet the needs of power enterprises for high-quality speech data. Insufficient intelligent decision support: The existing systems lack the ability to intelligently analyze and predict customer demands, and cannot identify potential risks and hot demand events in advance, resulting in lagging service responses. Low work order allocation efficiency: The existing work order allocation methods mainly rely on manual operations and lack intelligent allocation algorithms, resulting in low work order processing efficiency and unbalanced resource allocation. Unscientific service quality evaluation: The existing service quality evaluation methods lack scientificity and objectivity, and it is difficult to comprehensively reflect the timeliness, accuracy of customer demand processing, and customer satisfaction.

[0004] With the rapid development of technologies such as artificial intelligence, big data, and blockchain, power companies have ushered in new technological opportunities in customer service process processing: Multi-channel data integration: Through standardized interfaces such as RESTfulAPI, deep integration of multi-channel data such as WeChat, telephone, and landline is achieved to build a unified customer demand processing system. Intelligent speech recognition: Using advanced speech recognition algorithms (such as CTC loss function, Wiener filter algorithm, etc.) to improve the accuracy and efficiency of speech recognition, especially the voice data processing capabilities in complex environments. Intelligent work order allocation: Through intelligent algorithms (such as linear programming algorithms, blockchain technology, etc.), optimize the work order allocation plan to improve work order processing efficiency and resource utilization. Intelligent risk prediction: Using machine learning algorithms (such as BERT pre-training model, umbrella decision algorithm, etc.), intelligent analysis and risk prediction of customer demands are carried out to identify potential complaint risks and hot demand events in advance. Scientific service quality evaluation: Through the hierarchical analysis method and fuzzy comprehensive evaluation method, a scientific and quantitative service quality evaluation model is constructed to fully reflect the timeliness, accuracy and customer satisfaction of customer demand processing.

[0005] Although new technologies have brought new opportunities for the customer service process of power companies, they still face the following technical challenges in practical applications: Data security and privacy protection: The integration and sharing of multi-channel data increases the risk of data leakage and privacy leakage. How to achieve efficient use of data while ensuring data security is an important technical challenge. The solution includes the use of end-to-end AES encryption algorithm to encrypt call data during transmission and storage to ensure data security. Algorithm optimization and model update: With the diversification and complexity of customer demands, existing algorithms and models need to be continuously optimized and updated to adapt to new business needs and data changes. The solution includes updating machine learning models in real time through online learning algorithms, monitoring and evaluating model performance using model evaluation indicators (such as accuracy, recall rate, F1 value, etc.), and triggering the model update mechanism when the model performance is lower than the set threshold. System performance and scalability: With the increase in the number of customers and the expansion of business scale, the performance and scalability of the system face severe challenges. How to ensure the stability and efficiency of the system under high concurrency and large data volume is an urgent problem to be solved. The solution includes using caching technology to cache commonly used data to reduce the number of database queries; using a distributed architecture to balance the system load to multiple servers, improving the system's concurrent processing capabilities and ensuring the smoothness of user operations.

[0006] In the future, the power enterprise customer service process handling system will develop towards a more intelligent, automated, and personalized direction. By introducing more advanced artificial intelligence technologies and big data analysis methods, power enterprises can further improve the efficiency and quality of handling customer requests, achieving precision and personalization in customer service. At the same time, with the widespread application of blockchain technology, the transparency and traceability of the customer request handling process will be significantly improved, further enhancing customer trust and satisfaction. In addition, with the popularization of 5G technology and the widespread application of Internet of Things devices, power enterprises will be able to monitor and respond to customer requests in real time, providing a more efficient and convenient service experience. Summary of the Invention

[0007] The technical problem to be solved by the present invention is an offline multi-channel customer request handling system based on grid control. This model solves a series of technical problems in multi-channel customer request handling based on a hierarchical system architecture and multiple modules.

[0008] To solve the above technical problems, the technical solutions adopted by the present invention are as follows; An offline multi-channel customer request handling system based on grid control, characterized in that the system is based on a hierarchical system and implemented through the cooperation of multiple modules, specifically: The multiple modules are specifically: including a WeChat service channel module, an offline telephone voice channel module, and a grid service quality processing platform module; communication and data interaction are achieved between the modules through a standardized RESTful API data interface to build a unified customer request handling system; The hierarchical system is specifically: divided into a data collection layer, a data processing layer, a business logic layer, and a user interface layer; the data collection layer is responsible for collecting customer request data from various channels; the data processing layer cleans, transforms, and fuses the collected data; the business logic layer implements core business functions including but not limited to work order control and service quality analysis; the user interface layer provides an operation and display interface for users.

[0009] As a preferred technical solution of the present invention, the WeChat service channel module is specifically: the WeChat service channel module is based on the WeChat group intelligent customer service platform and works in coordination through the WeChat mini-program end micro-application and the WeChat group end robot; in terms of customer request recognition, using the named entity recognition algorithm in natural language processing technology and combining with the BERT pre-training model, in-depth semantic analysis is performed on the customer request text to accurately identify the key information therein, including but not limited to time, location, and problem type, to achieve automatic classification and rapid response of customer requests; Based on a newly developed machine learning algorithm, analyze the customer's historical requests and establish a customer demand prediction model; this model predicts the requests that the customer may put forward in advance and pushes relevant service information according to factors including but not limited to the customer's historical request habits and time patterns.

[0010] As a preferred technical solution of the present invention, the newly developed machine learning algorithm is as follows: This algorithm is an intelligent model specifically designed to process time-sequential data. Its core design revolves around a "dynamic storage unit" and a "multiple regulation mechanism". The model uses a "data screening module" to determine which new information needs to be incorporated into the storage unit, and at the same time uses an "information update module" to dynamically adjust the historical data in the storage unit to ensure that its content is always highly relevant to the current task. In addition, the model is also equipped with an "output regulation module" to control how the data in the storage unit affects the current calculation result. This design enables the model to avoid information loss and redundancy when processing data with a long time span, and at the same time analyzes each data point in the sequence step by step through a "step-by-step processing mechanism" to ensure the integrity and continuity of information. Another key feature of the model is the "adaptive feedback mechanism", which dynamically adjusts the internal state and output result according to the change of input data, so as to flexibly respond to different task requirements. Through this structure, the model can efficiently capture hidden patterns in complex time-series data and generate accurate prediction results.

[0011] As a preferred technical solution of the present invention, the offline telephone voice channel module is specifically: a work mobile phone voice channel APP, which uses a newly developed speech recognition algorithm. In the speech acquisition stage, the Wiener filtering algorithm is used for adaptive filtering to eliminate environmental noise in real time and improve the speech quality. In the speech recognition process, the CTC loss function is used to optimize the model training to improve the accuracy of speech recognition. The call data is encrypted during transmission and storage through an end-to-end AES encryption algorithm.

[0012] As a preferred technical solution of the present invention, the algorithm model is specifically: This model is an intelligent computing framework that combines multi-level feature extraction and sequence data analysis capabilities. Its core architecture consists of two parts: The first part is the "multi-level feature extraction module", which captures the spatial features in the data layer by layer through a "local perception mechanism" and extracts representative information patterns from the original data. The second part is the "sequence data processing module", which gradually analyzes the temporal order relationship of the data through a "dynamic state transfer mechanism" to ensure that the model captures the long-term dependencies and context information in the data. In the "multi-level feature extraction module", the model performs hierarchical processing on the input data through the "feature mapping unit", and each layer extracts features at different abstraction levels and integrates this information into a higher-level representation through the "feature aggregation mechanism". In the "sequence data processing module", the model dynamically adjusts the internal state through the "state update unit" to effectively process the data at each time step. The module is also equipped with an "information screening and integration mechanism" to selectively retain and update key information according to the current input and historical state, thus avoiding interference from irrelevant data.

[0013] As a preferred technical solution of the present invention, the grid service quality processing platform module is specifically as follows: It includes a data fusion sub-module, a work order control sub-module, and a service quality analysis sub-module, specifically as follows: Data fusion sub-module: Obtain customer demand data from multiple channels including but not limited to WeChat, work mobile phone voice, and power supply station landline voice; Use data cleaning algorithms to detect and remove outliers in the data, detect outliers and clean them; Through the association analysis algorithm based on the graph database, establish the association relationship between multi-channel data, realize the deep fusion of data, and provide a high-quality data basis for subsequent analysis and processing; Work order control sub-module: Apply an intelligent work order allocation algorithm, according to factors including but not limited to customer demand type, urgency, geographical location, and the workload of grid service personnel, optimize the work order allocation plan through the linear programming algorithm, and use the consortium chain architecture in blockchain technology to record and trace the work order processing process. Each work order operation forms an immutable block record to ensure the integrity and authenticity of the data; Service quality analysis sub-module: Construct a service quality evaluation model. This model adopts an evaluation framework that combines a "hierarchical weight allocation module" and a "fuzzy comprehensive evaluation module". Use the "hierarchical weight allocation module" to allocate weights to multiple evaluation indicators for customer demand processing, including but not limited to timeliness, accuracy, and customer satisfaction. Determine the weight of timeliness to be 0.4, the weight of accuracy to be 0.3, and the weight of customer satisfaction to be 0.3 through the "expert scoring mechanism"; Use the "fuzzy comprehensive evaluation module" to quantitatively evaluate each indicator, and divide the evaluation results into five levels: "excellent, good, medium, poor, and bad"; Through the clustering analysis algorithm, cluster the service quality data in different regions and different time periods. This algorithm divides the data points into different clusters through iterative calculation, and each cluster represents a service quality type, find out the differences and change trends of service quality, and provide data support for optimizing service strategies.

[0014] As a preferred technical solution of the present invention, the system uses a risk prediction model to evaluate the risks of customer demands. This model is trained on a large amount of historical customer demand data to learn the relationship between different demand characteristics and risk levels, and identify potential complaint risks and hot demand events in advance; Through the umbrella decision algorithm, automatically generate corresponding coping strategies according to the risk assessment results. This algorithm constructs an umbrella decision structure according to factors including but not limited to risk level and demand type. Each node represents a decision condition, and each branch represents a decision result; For hot demand events, start emergency plans including but not limited to increasing customer service staff and organizing special treatment teams to achieve intelligent decision-making.

[0015] As a preferred technical solution of the present invention, based on strict performance indicators, the system enables the response time of conventional data queries to be less than 1 second, the response time of fuzzy queries to be less than 2 seconds, 90% of the interface switching response time to be no more than 1.5 seconds, and the response time of other operations to be no more than 2 seconds. To achieve this goal, caching technology is adopted to cache frequently used data and reduce the number of database queries; a distributed architecture is adopted to balance the system load across multiple servers, improve the concurrent processing ability of the system, and ensure the smoothness of user operations.

[0016] As a preferred technical solution of the present invention, based on good scalability, the system can be easily connected to new customer service channels and business systems, and achieve seamless docking with third-party systems through standardized data interfaces and protocols. At the same time, the system is compatible with a variety of hardware devices and operating systems, supporting multiple operating systems including but not limited to Windows, Linux, iOS, and Android, as well as multiple hardware devices including but not limited to desktop computers, laptops, tablets, and smartphones, ensuring the wide applicability of the system.

[0017] As a preferred technical solution of the present invention, the system regularly optimizes and updates the algorithms and models used. Through online learning algorithms, the machine learning models are updated in real time. This algorithm iteratively trains on new customer demand data and continuously adjusts the model parameters to adapt to changes in customer demands and the needs of business development. Model evaluation metrics including but not limited to accuracy, recall, and F1-score are used to monitor and evaluate the model performance. When the evaluation metrics of the model are lower than the set threshold, the model update mechanism is triggered and the model is updated using incremental learning and retraining methods. At the same time, an algorithm optimization knowledge base is established to summarize and record the optimization experiences and methods of the algorithms, providing reference for subsequent algorithm optimization.

[0018] The beneficial effects of adopting the above technical solutions are as follows: Through technical means such as multi-channel data fusion, intelligent speech recognition, intelligent work order allocation, intelligent risk prediction, and scientific service quality evaluation, this technology significantly improves the efficiency and quality of handling customer demands in power enterprises, optimizes the work order allocation and processing process, enhances service quality and customer satisfaction, identifies potential risks and hot demands in advance, improves system performance and scalability, ensures data security and privacy protection, and through continuous optimization and update, ensures that the system can adapt to changing business needs. These benefits enable power enterprises to better meet customer needs, improve customer satisfaction, and optimize the power business environment. Description of the Drawings

[0019] Figure 1 : Model flow chart; Figure 2 : Overall structure diagram; Figure 3 : Business architecture diagram; Figure 4 : Application architecture diagram; Figure 5 : Data architecture diagram; Figure 6 : Technical architecture diagram; Figure 7 : Security architecture diagram; Figure 8 : Deployment architecture diagram. Detailed implementation manners

[0020] Now, the principles of the present disclosure will be described with reference to several exemplary embodiments shown in the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the description of these embodiments is only for facilitating those skilled in the art to better understand and thereby implement the present disclosure, rather than limiting the scope of the present disclosure in any way. In the description of the following embodiments, specific details such as specific system structures and technologies are proposed for illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0021] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, components.

[0022] Embodiment 1: Overview of overall steps Refer to the appendix Figure 1-2 The operation process of the offline multi-channel customer demand processing system based on grid control is centered around a hierarchical system and multi-module collaboration. The specific process is as follows: 1. Data collection and preprocessing Refer to the appendix Figure 3 , the system first collects customer demand data from multiple channels (including WeChat, work mobile phone voice, power supply station landline voice, etc.) through the data collection layer. The WeChat service channel module uses the intelligent customer service platform in the WeChat group, and the micro application on the WeChat applet end and the robot on the WeChat group end work together to obtain the demand information feedback by customers in the WeChat group in real time; the offline phone voice channel module collects the voice data of the communication between the area manager and the customer through the work mobile phone voice channel APP. The data collection layer ensures that the customer demand data of all channels can enter the system in real time and accurately.

[0023] 2. Data Cleaning and Fusion The collected data enters the data processing layer. The system first preprocesses the data using data cleaning algorithms to detect and remove outliers, ensuring the accuracy and integrity of the data. Subsequently, the system deeply fuses the data from different channels through an association analysis algorithm based on a graph database, establishes the association relationships between multi-channel data, and forms a unified customer demand dataset. This step provides a high-quality data foundation for subsequent analysis and processing.

[0024] 3. Customer Demand Identification and Classification In the business logic layer, the system conducts in-depth semantic analysis on the customer demand text through the named entity recognition algorithm in natural language processing technology, combined with the BERT pre-trained model, accurately identifies the key information therein (such as time, location, problem type, etc.), and realizes the automatic classification and rapid response of customer demands. For voice data, the system adopts a newly developed speech recognition algorithm, uses the Wiener filtering algorithm for adaptive filtering to eliminate environmental noise, and optimizes the model training through the CTC loss function to improve the accuracy of speech recognition. 4. Work Order Control and Allocation The system optimizes the work order allocation plan through the work order control sub-module using an intelligent work order allocation algorithm, based on the type, urgency, geographical location of customer demands, and the workload of grid service personnel. During the work order allocation process, the system uses the linear programming algorithm to ensure that work orders can be quickly and reasonably allocated to the most suitable processing personnel. At the same time, the system adopts the consortium chain architecture in blockchain technology to record and trace the work order processing process, and each work order operation forms an immutable block record to ensure the integrity and authenticity of the data.

[0025] 5. Service Quality Analysis and Evaluation The system constructs a service quality evaluation model through the service quality analysis sub-module, adopting an evaluation framework that combines a hierarchical weight allocation module and a fuzzy comprehensive evaluation module. First, the system uses the hierarchical weight allocation module to allocate weights to multiple evaluation indicators such as the timeliness, accuracy, and customer satisfaction of customer demand processing, and determines the weights of each indicator through an expert scoring mechanism (such as the timeliness weight is 0.4, the accuracy weight is 0.3, and the customer satisfaction weight is 0.3). Subsequently, the system uses the fuzzy comprehensive evaluation module to quantitatively evaluate each indicator and divides the evaluation results into five levels: "excellent, good, medium, poor, very poor". In addition, the system uses the clustering analysis algorithm to cluster the service quality data in different regions and different time periods to find the differences and change trends of service quality, providing data support for optimizing service strategies.

[0026] 6. Risk Prediction and Intelligent Decision-making The system utilizes a risk prediction model. By training on a large amount of historical customer demand data, it learns the relationships between different demand characteristics and risk levels, and identifies potential complaint risks and hot demand events in advance. Based on the risk assessment results, the system automatically generates corresponding response strategies through the umbrella decision-making algorithm. The umbrella decision-making algorithm constructs an umbrella decision-making structure according to factors such as risk levels and demand types. Each node represents a decision condition, and each branch represents a decision result. For hot demand events, the system can initiate emergency response plans (such as increasing customer service staff, organizing special handling teams, etc.) to achieve intelligent decision-making and rapid response.

[0027] 7. System Performance Optimization and Expansion Reference Appendix Figure 8 Based on strict performance indicators, the system adopts caching technology and a distributed architecture to ensure that the response time for regular data queries is less than 1 second, the response time for fuzzy queries is less than 2 seconds, the response time for 90% of interface switches is no more than 1.5 seconds, and the response time for other operations is no more than 2 seconds. The system balances the load across multiple servers through a distributed architecture, improving the system's concurrent processing ability and ensuring the smoothness of user operations. In addition, the system has good scalability and can easily connect to new customer service channels and business systems, achieving seamless docking with third-party systems through standardized data interfaces and protocols. The system is compatible with a variety of hardware devices and operating systems, supporting multiple operating systems such as Windows, Linux, iOS, and Android, as well as various hardware devices such as desktop computers, laptops, tablets, and smartphones, ensuring the wide applicability of the system.

[0028] 8. Algorithm Optimization and Model Update The system regularly optimizes and updates the algorithms and models it uses. Through online learning algorithms, it updates the machine learning models in real time. The online learning algorithm iteratively trains on new customer demand data and continuously adjusts the model's parameters to adapt to changes in customer demands and the needs of business development. The system uses model evaluation metrics (such as accuracy, recall rate, F1 value, etc.) to monitor and evaluate the model's performance. When the evaluation metrics of the model are lower than the set thresholds, the system triggers the model update mechanism and updates the model using incremental learning and retraining methods. At the same time, the system establishes an algorithm optimization knowledge base to summarize and record the optimization experiences and methods of the algorithms, providing reference for subsequent algorithm optimization.

[0029] 9. Data Security and Privacy Protection During the entire operation process, the system uses the end-to-end AES encryption algorithm to encrypt call data during transmission and storage to ensure data security. At the same time, the system uses blockchain technology to record and trace the work order processing process. Each work order operation forms an unalterable block record, further enhancing the data integrity and privacy protection capabilities.

[0030] Implementation Example 2: Customer Complaint Processing and Service Quality Evaluation on WeChat Service Channel Reference Figure 4 A power company was facing difficulties in its WeChat service channel. It received more than 5,000 customer requests through WeChat groups every month. Due to the lack of intelligent means, the average processing time was 2 days, and customer satisfaction was only 60%. To improve the situation, the company introduced this technology.

[0031] 1. Data collection and preprocessing: The system uses the WeChat group intelligent customer service platform to collect customer demands in real time. For example, at 3:20 pm on August 15, a customer reported in the WeChat group: "My home is in Unit 3, Building 1, Sunshine Community, Room 501. The power outage occurred at 3 pm today. When will it be restored?" The data collection layer quickly transmits the information to the data processing layer, and uses a rule-based and statistical cleaning algorithm to remove duplicate and invalid information. After statistical analysis, duplicate demands account for about 5%, invalid information accounts for about 3%, and valid information enters the subsequent process after cleaning.

[0032] 2. Reference Figure 6 , Customer demand identification and classification: The system uses the named entity recognition algorithm in natural language processing technology, combined with the BERT pre-training model, to perform in-depth semantic analysis on customer demand text. The model calculates the word vector similarity and identifies "power outage" as the problem type, "3 pm" as the time, and "No. 501, Unit 3, Building 1, Sunshine Community" as the location. Based on the preset classification rules, the system automatically classifies the demand as "power outage problem" and generates a work order with the work order number 20240815001.

[0033] 3. Work order control and allocation: The intelligent work order allocation algorithm allocates work orders based on the type of demand (power outage problem), urgency (high), geographic location (Sunshine Community), and the workload of grid service personnel (assessed by counting the number of work orders processed by each service personnel in the past week). Assume that there are currently three district managers responsible for Sunshine Community. Manager A processed 20 work orders in the past week, Manager B processed 15, and Manager C processed 18. According to the load balancing formula, it is calculated that Manager B's load is relatively low, so the work order is assigned to Manager B. During the work order processing process, blockchain technology is used to record each operation step. Each block contains information such as timestamp, operation content, and operator to ensure that the data is complete and traceable.

[0034] 4. Service Quality Analysis and Evaluation: The service quality analysis sub-module evaluates the timeliness, accuracy, and customer satisfaction of handling customer requests through a hierarchical weight allocation module. The timeliness evaluation is based on the duration from the generation to the completion of the work order. Let the specified processing duration be T (for power outage problems, it is required to be completed within 2 hours), and the actual processing duration be t (this work order was completed in 1.5 hours). Then the timeliness score = 1 - (t / T) = 1 - (1.5 / 2) = 0.25 (full score is 1 point), and after adjustment, it is 0.9 (evaluated comprehensively considering other factors); the accuracy is scored according to the matching degree between the processing result and the customer request, with a full score of 1 point. This work order was processed accurately and received a score of 0.85; the customer satisfaction is scored through customer feedback, with a full score of 1 point, and this customer's score is 0.88. Using the fuzzy comprehensive evaluation module, according to the preset membership function and weight vector, the comprehensive score is calculated, corresponding to the "excellent" level. Through the clustering analysis algorithm, the power outage request data in this area for the past month is analyzed, and it is found that power outage problems are concentrated in specific areas and occur frequently. It is recommended to strengthen equipment inspections.

[0035] 5. Risk Prediction and Intelligent Decision-making: The risk prediction model analyzes the power outage request data in this area for the past three months, uses machine learning algorithms (such as the logistic regression model), and combines factors such as historical power outage times and equipment failure frequencies to predict that the power outage risk probability in this area for the next week is 30%. The umbrella-shaped decision-making algorithm generates coping strategies according to the risk level (high risk) and request type (power outage problem), such as increasing the inspection frequency from 2 times a week to 4 times a week; notifying customers of power outage information in advance and releasing it through channels such as text messages and WeChat official accounts.

[0036] 6. System Performance Optimization and Expansion: The system uses caching technology to cache the power outage information of Sunshine Community for the past month, reducing the number of database queries. After testing, the average response time for work order queries is 0.5 seconds, which is less than 1 second. Through a distributed architecture, the load is balanced across 3 servers. In the case of high concurrency (simulating handling 100 work orders simultaneously), the system runs stably and the response time has no obvious delay.

[0037] 7. Algorithm Optimization and Model Update: The system updates the machine learning model weekly based on new power outage request data through an online learning algorithm. For example, according to the new power outage data this week, the parameters of the logistic regression model are adjusted to improve prediction accuracy. The performance of the model is monitored through model evaluation metrics (accuracy, recall rate, F1 value, etc.). When the F1 value is lower than 0.8 (the set threshold), the model update mechanism is triggered, and the model is updated by means of incremental learning or retraining.

[0038] 8. Reference Appendix Figure 7, Data Security and Privacy Protection: The system encrypts customer request data through the AES encryption algorithm, and the encryption key is changed regularly. During the transmission process, the SSL / TLS protocol is adopted to ensure data security; when storing, the encrypted data is stored in the database, and only authorized personnel can decrypt and access it to ensure the security of data during transmission and storage.

[0039] Implementation Example 3: Speech Recognition and Work Order Allocation for Offline Phone Voice Channels A certain power company receives approximately 3,000 customer phone requests per month through work mobile phones and power supply station landlines. Due to the low speech recognition accuracy (about 70%) and inefficient work order allocation, customer requests are not processed in a timely manner, with an average processing duration of 1.5 days and a customer satisfaction of 75%. The company introduced this technology for improvement.

[0040] 1. Data Collection and Preprocessing: The system collects voice data through the work mobile phone voice channel APP and the power supply station landline voice channel. For example, at 10:10 am on September 10th, a customer called and reported: "My home is in Group 2, Xingfu Village, and the voltage is unstable and often trips." The data collection layer transmits the voice data to the data processing layer, and uses the Wiener filtering algorithm for adaptive filtering. According to the statistical characteristics of the signal and noise, the filtering coefficient is adjusted in real time to eliminate environmental noise and improve the voice quality. After testing, the signal-to-noise ratio of the processed voice has increased by 20%.

[0041] 2. Reference Appendix Figure 6 , Speech Recognition and Request Classification: The system converts voice data into text data through a speech recognition model optimized based on the CTC loss function. During model training, a large number of voice samples containing voltage problems are used to identify "unstable voltage" as the problem type, "often trips" as the specific description, and "Group 2, Xingfu Village" as the geographical location. The system automatically classifies the request as a "voltage problem" and generates a work order number 20240910001.

[0042] 3. Work Order Control and Allocation: The intelligent work order allocation algorithm allocates work orders based on the request type (voltage problem), urgency (medium), geographical location (Group 2, Xingfu Village), and the workload of grid service personnel (counting the number of work orders processed in the past week). Suppose among the 3 substation managers responsible for Xingfu Village, Manager D processed 16 work orders in the past week, Manager E processed 14 work orders, and Manager F processed 17 work orders. According to the load balancing formula calculation, the work order is allocated to Manager E. During the work order processing, the blockchain technology is used to record each operation step to ensure the integrity and traceability of the data.

[0043] 4. Service Quality Analysis and Evaluation: The service quality analysis sub-module uses the hierarchical weight allocation module to evaluate the timeliness, accuracy, and customer satisfaction of handling customer requests. For timeliness evaluation, the specified processing duration is T (for voltage problems, it is specified to be completed within 4 hours), and the actual processing duration is t (this work order was completed in 3 hours). The timeliness score = 1 - (t / T) = 1 - (3 / 4) = 0.25 (full score of 1 point), and after adjusting for other factors, it is adjusted to 0.8 points; the accuracy is scored according to the matching degree between the processing result and the customer request, with a full score of 1 point, and this work order is accurately processed with a score of 0.9 points; the customer satisfaction is scored through customer feedback, with a full score of 1 point, and this customer's score is 0.85 points. Using the fuzzy comprehensive evaluation module, the comprehensive score is calculated, corresponding to the "good" level. Through the clustering analysis algorithm, the voltage request data in this area for the past month is analyzed, and it is found that the voltage problems in this area are concentrated, and it is recommended to upgrade the equipment.

[0044] 5. Risk Prediction and Intelligent Decision-making: The risk prediction model analyzes the voltage request data in this area for the past three months, uses machine learning algorithms (such as decision tree models), and combines factors such as historical voltage fluctuation data and equipment aging degree to predict that the risk probability of voltage problems in this area in the next week is 40%. The umbrella-shaped decision-making algorithm generates coping strategies according to the risk level (medium risk) and request type (voltage problem), such as arranging the equipment upgrade plan in advance and increasing the inspection frequency from 3 times a week to 5 times a week.

[0045] 6. System Performance Optimization and Expansion: The system uses caching technology to cache the voltage problem information in Xingfu Village for the past month, reducing the number of database queries. The average response time for work order queries is 0.6 seconds, which is less than 1 second. Through a distributed architecture, the load is balanced across 3 servers, and in the case of high concurrency (simulating handling 80 work orders simultaneously), the system runs stably and the response time has no obvious delay.

[0046] 7. Algorithm Optimization and Model Update: The system updates the machine learning model weekly according to new voltage request data through an online learning algorithm. For example, according to the new voltage data this week, the parameters of the decision tree model are adjusted to improve the prediction accuracy. The performance of the model is monitored through model evaluation metrics (accuracy, recall rate, F1 value, etc.). When the F1 value is lower than 0.75 (the set threshold), the model update mechanism is triggered, and the model is updated by means of incremental learning or retraining.

[0047] 8. Reference Appendix Figure 7 , Data Security and Privacy Protection: The system encrypts customer voice data through the AES encryption algorithm, and the encryption key is replaced regularly. During the transmission process, the SSL / TLS protocol is used to ensure data security; when storing, the encrypted data is stored in the database, and only authorized personnel can decrypt and access it to ensure the security of data during transmission and storage.

[0048] Implementation Example 4: Multi-channel Data Fusion and Risk Prediction of Grid Service Quality Processing Platform Reference Appendix Figure 3 and 5 , a certain power company hopes to integrate customer demand data from multiple channels such as WeChat, phone, and landline to improve service quality processing capabilities. Before integration, the data of each channel was scattered and difficult to control uniformly, and the complaint rate reached 10%. This technology was introduced to build a grid service quality processing platform.

[0049] 1. Data Collection and Preprocessing: The system collects customer demand data through multiple channels such as WeChat, work mobile phone voice, and power supply station landline voice. For example, on October 5th, a customer feedback through WeChat: "I live in Room 702, Unit 2, Building 3, Harmony Community, and the electricity bill is abnormally high." At the same time, another customer feedback through phone: "I live in Room 702, Unit 2, Building 3, Harmony Community, and the electricity meter seems to be broken." The data collection layer transmits the multi-channel data to the data processing layer, and uses the density-based spatial clustering algorithm (DBSCAN) to remove abnormal data. After analysis, the proportion of abnormal data is about 4%.

[0050] 2. Data Fusion and Demand Classification: The system deeply fuses the data from different channels through an association analysis algorithm based on a graph database. By establishing an entity relationship graph, it is found that "abnormally high electricity bill" and "broken electricity meter" may be the same problem, and the correlation degree reaches 0.8 (the set threshold is 0.6). The system automatically classifies the demand as "electricity meter problem" and generates a work order number 20241005001.

[0051] 3. Work Order Control and Assignment: The intelligent work order assignment algorithm assigns work orders according to the demand type (electricity meter problem), urgency (high), geographical location (Harmony Community), and the workload of grid service personnel (counting the number of work orders processed in the past week). Assume that among the 3 district managers responsible for Harmony Community, Manager G processed 18 work orders in the past week, Manager H processed 16 work orders, and Manager I processed 15 work orders. According to the load balancing formula calculation, the work order is assigned to Manager I. During the work order processing, the blockchain technology is used to record each operation step to ensure the integrity and traceability of the data.

[0052] 4. Service Quality Analysis and Evaluation: The service quality analysis sub-module uses the hierarchical weight allocation module to evaluate the timeliness, accuracy, and customer satisfaction of handling customer requests. For timeliness evaluation, the specified processing duration is T (for electricity meter problems, it is specified to be completed within 3 hours), and the actual processing duration is t (this work order was completed in 2 hours). The timeliness score = 1 - (t / T) = 1 - (2 / 3) ≈ 0.33 (full score is 1 point), and after adjusting for other factors, it is 0.85 points; the accuracy is scored according to the matching degree between the processing result and the customer request, with a full score of 1 point, and this work order is accurately processed with a score of 0.9 points; the customer satisfaction is scored through customer feedback, with a full score of 1 point, and this customer's score is 0.88 points. Using the fuzzy comprehensive evaluation module, the comprehensive score is calculated, corresponding to the "excellent" level. Through the clustering analysis algorithm, the electricity meter request data in this area for the past month is analyzed, and it is found that the electricity meter problems in this area are concentrated, and it is recommended to replace the equipment.

[0053] 5. Risk Prediction and Intelligent Decision-making: The risk prediction model analyzes the electricity meter request data in this area for the past three months, uses machine learning algorithms (such as the random forest model), and combines factors such as historical electricity meter failure data and electricity meter service life to predict that the risk probability of electricity meter problems in this area in the next week is 35%. The umbrella-shaped decision-making algorithm generates coping strategies according to the risk level (high risk) and request type (electricity meter problem), such as implementing the equipment replacement plan in advance and increasing the inspection frequency from 2 times a week to 4 times a week.

[0054] 6. System Performance Optimization and Expansion: The system uses caching technology to cache the electricity meter problem information in Harmony Community for the past month, reducing the number of database queries. The average response time for work order queries is 0.7 seconds, which is less than 1 second. Through a distributed architecture, the load is balanced across 3 servers, and in the case of high concurrency (simulating handling 90 work orders simultaneously), the system runs stably and the response time has no obvious delay.

[0055] 7. Algorithm Optimization and Model Update: The system updates the machine learning model weekly according to new electricity meter request data through the online learning algorithm. For example, according to the new electricity meter data this week, the parameters of the random forest model are adjusted to improve the prediction accuracy. The model performance is monitored through model evaluation metrics (accuracy, recall rate, F1 value, etc.). When the F1 value is lower than 0.8 (the set threshold), the model update mechanism is triggered, and the model is updated by means of incremental learning or retraining.

[0056] 8. Data Security and Privacy Protection: The system encrypts customer request data through the AES encryption algorithm, and the encryption key is replaced regularly. During the transmission process, the SSL / TLS protocol is used to ensure data security; when storing, the encrypted data is stored in the database, and only authorized personnel can decrypt and access it to ensure the security of data during transmission and storage.

[0057] As described above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. An offline multi-channel customer demand processing system based on grid control, characterized in that: The system is based on a hierarchical system and is implemented using multi-module cooperation, specifically: The multiple modules are specifically: WeChat service channel module, offline telephone voice channel module and grid service quality processing platform module; the modules communicate and interact with each other through a standardized RESTful API data interface to build a unified customer demand processing system; The layered system is specifically divided into: data collection layer, data processing layer, business logic layer and user interface layer; the data collection layer is responsible for collecting customer demand data from various channels; the data processing layer cleans, converts and integrates the collected data; the business logic layer implements core business functions including but not limited to work order control and service quality analysis; the user interface layer provides users with an operation and display interface.

2. According to claim 1, an offline multi-channel customer demand processing system based on grid control is characterized in that: The WeChat service channel module is specifically as follows: the WeChat service channel module is based on the WeChat group intelligent customer service platform, and works in collaboration through the WeChat applet-side micro-application and the WeChat group-side robot; in terms of customer demand identification, the named entity recognition algorithm in the natural language processing technology is combined with the BERT pre-training model to perform in-depth semantic analysis on the customer demand text, accurately identify the key information therein, including but not limited to time, location, and question type, and realize automatic classification and rapid response to customer demands; Based on the newly developed machine learning algorithm, we analyze customers’ historical demands and build a customer demand prediction model. This model predicts the demands that customers may raise in advance and pushes relevant service information based on factors including but not limited to their historical demand habits and time patterns.

3. According to claim 2, the offline multi-channel customer demand processing system based on grid control is characterized in that: The newly developed machine learning algorithm is as follows: the algorithm is an intelligent model specifically used to process time-sequential data, and its core design revolves around a "dynamic storage unit" and a "multiple regulation mechanism". The model uses a "data screening module" to determine which new information needs to be included in the storage unit, and uses an "information update module" to dynamically adjust the historical data in the storage unit to ensure that its content is always highly relevant to the current task. In addition, the model is also equipped with an "output regulation module" to control how the data in the storage unit affects the current calculation results. This design allows the model to avoid information loss and redundancy when processing data with a long time span, and at the same time, through a "step-by-step processing mechanism", each data point in the sequence is gradually analyzed to ensure the integrity and continuity of the information. Another key feature of the model is the "adaptive feedback mechanism", which dynamically adjusts the internal state and output results according to changes in the input data, so as to flexibly respond to different task requirements. Through this structure, the model can efficiently capture hidden patterns in complex time series data and generate accurate prediction results.

4. The offline multi-channel customer demand processing system based on grid control according to claim 1 is characterized in that: The offline telephone voice channel module is specifically as follows: a work mobile phone voice channel APP, which adopts a newly developed voice recognition algorithm; in the voice collection stage, the Wiener filter algorithm is used for adaptive filtering to eliminate environmental noise in real time and improve voice quality; in the voice recognition process, the CTC loss function is used to optimize model training to improve the accuracy of voice recognition; through the end-to-end AES encryption algorithm, the call data is encrypted during transmission and storage.

5. The grid-controlled offline multi-channel customer demand processing system according to claim 4, characterized in that: The algorithm model is specifically as follows: the model is an intelligent computing framework that combines multi-level feature extraction and sequence data analysis capabilities, and its core architecture consists of two parts: the first part is a "multi-level feature extraction module", which captures spatial features in the data layer by layer through a "local perception mechanism" and extracts representative information patterns from the original data; the second part is a "sequence data processing module", which gradually analyzes the temporal order relationship of the data through a "dynamic state transfer mechanism" to ensure that the model captures the long-term dependencies and contextual information in the data; in the "multi-level feature extraction module", the model processes the input data in layers through a "feature mapping unit", extracts features of different abstract levels at each layer, and integrates this information into a higher-level representation through a "feature aggregation mechanism"; in the "sequence data processing module", the model dynamically adjusts the internal state through a "state update unit" so that the data at each time step is effectively processed; the module is also equipped with an "information screening and integration mechanism" to selectively retain and update key information based on the current input and historical state, thereby avoiding interference from irrelevant data.

6. The offline multi-channel customer demand processing system based on grid control according to claim 1, characterized in that: The grid service quality processing platform module specifically includes: a data fusion submodule, a work order control submodule, and a service quality analysis submodule, specifically: Data fusion submodule: obtain customer demand data from multiple channels including but not limited to WeChat, work mobile phone voice, and power supply station landline voice; use data cleaning algorithms to detect and remove outliers in the data, detect outliers and clean them; establish associations between multi-channel data through association analysis algorithms based on graph databases, achieve deep data fusion, and provide a high-quality data foundation for subsequent analysis and processing; Work order control submodule: Use intelligent work order allocation algorithm to optimize the work order allocation plan through linear programming algorithm based on factors including but not limited to customer demand type, urgency, geographic location and workload of grid service personnel. Use the alliance chain architecture in blockchain technology to record and trace the work order processing process. Each work order operation forms an unalterable block record to ensure the integrity and authenticity of the data. Service quality analysis submodule: Construct a service quality evaluation model, which adopts the evaluation framework of "hierarchical weight allocation module" combined with "fuzzy comprehensive evaluation module". The "hierarchical weight allocation module" is used to assign weights to multiple evaluation indicators of customer demand processing, including but not limited to timeliness, accuracy, and customer satisfaction. The "expert scoring mechanism" determines that the weight of timeliness is 0.4, the weight of accuracy is 0.3, and the weight of customer satisfaction is 0.3; the "fuzzy comprehensive evaluation module" is used to quantitatively evaluate each indicator, and the evaluation results are divided into five levels: "excellent, good, medium, poor, and bad"; the cluster analysis algorithm is used to cluster the service quality data of different regions and time periods. The algorithm divides the data points into different clusters through iterative calculations. Each cluster represents a type of service quality, finds out the differences and changing trends of service quality, and provides data support for optimizing service strategies.

7. The grid-controlled offline multi-channel customer demand processing system according to claim 1, characterized in that: The system uses a risk prediction model to conduct risk assessment on customer demands. The model is trained on a large amount of historical customer demand data, learns the relationship between different demand characteristics and risk levels, and identifies potential complaint risks and hot demand events in advance; through the umbrella decision algorithm, the corresponding response strategy is automatically generated according to the risk assessment results. The algorithm constructs an umbrella decision structure based on factors including but not limited to risk level and demand type. Each node represents a decision condition and each branch represents a decision result; for hot demand events, the emergency plan is activated, including but not limited to increasing customer service personnel and organizing special processing teams to achieve intelligent decision-making.

8. The grid-controlled offline multi-channel customer demand processing system according to claim 1, characterized in that: Based on strict performance indicators, the system ensures that the response time for regular data queries is less than 1 second, the response time for fuzzy queries is less than 2 seconds, the response time for 90% interface switching is no more than 1.5 seconds, and the response time for other operations is no more than 2 seconds. To achieve this goal, caching technology is used to cache commonly used data to reduce the number of database queries. A distributed architecture is adopted to balance the system load to multiple servers, improve the system's concurrent processing capabilities, and ensure the smoothness of user operations.

9. The offline multi-channel customer demand processing system based on grid control according to claim 1, characterized in that: Based on good scalability, the system can easily access new customer service channels and business systems, and achieve seamless integration with third-party systems through standardized data interfaces and protocols. At the same time, the system is compatible with a variety of hardware devices and operating systems, supporting multiple operating systems including but not limited to Windows, Linux, iOS and Android, as well as a variety of hardware devices including but not limited to desktops, laptops, tablets and smart phones, ensuring the wide applicability of the system.

10. The grid-controlled offline multi-channel customer demand processing system according to claim 1, characterized in that: The system regularly optimizes and updates the algorithms and models used, and updates the machine learning model in real time through the online learning algorithm. The algorithm iteratively trains on new customer demand data and continuously adjusts the model parameters to adapt to changes in customer demands and business development needs. Model evaluation indicators including but not limited to accuracy, recall, and F1 value indicators are used to monitor and evaluate model performance. When the model's evaluation indicator is lower than the set threshold, the model update mechanism is triggered and the model is updated by incremental learning and retraining. At the same time, an algorithm optimization knowledge base is established to summarize and record the algorithm's optimization experience and methods, providing a reference for subsequent algorithm optimization.

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