Power customer service risk intelligent control optimization method and system

Abstract

The invention discloses a power customer service risk intelligent control optimization method and system, and relates to the technical field of power customer services, and the method comprises the following steps: obtaining real-time user risk data, processing the risk data through a pre-constructed dynamic risk field model, and generating a risk abrupt change signal and an optimal intervention path; in response to the risk mutation signal, calling a risk semantic unit library to generate a corresponding intervention verbal skill; based on the optimal intervention path, the intervention verbal skill is pushed to the telephone operator terminal through the cloud assistant; and acquiring a feedback result after the telephone operator executes the intervention telephone, and optimizing the knowledge graph structure. The method is used for solving the problems that emotion analysis and dynamic risk assessment mechanisms are lacked in a complex context, and customer emotion changes cannot be recognized and responded in real time.

Description

Technical Field

[0001] This invention relates to the field of power customer service technology, and more specifically, to a method and system for intelligent control and optimization of power customer service risks. Background Technology

[0002] With the gradual advancement of intelligent power services, the demand for customer service is increasing. Traditional customer service systems typically rely on simple rules and manual intervention, making it difficult to cope with increasingly complex customer needs and unexpected risk events. Intelligent customer service systems based on speech recognition and natural language processing technologies can improve response efficiency, but problems such as untimely responses and inaccurate script generation still exist. Therefore, how to combine the specific needs of the power industry with accurate risk assessment and automated script generation to achieve efficient and personalized customer service is a technical challenge in this field.

[0003] For example, the invention patent with announcement number CN114581097A discloses an intelligent power customer service method based on artificial intelligence, which includes the following steps: S1, power demanders establish a communication connection with the customer service module through the power demand terminal; S2, power demanders transmit their power demand to the customer service module through the power demand terminal via text, voice, or video; S3, the customer service module obtains the power instruction through the intelligent analysis module and sends it to the information confirmation module; S4, the information confirmation module feeds back the corresponding power instruction to the power demand terminal. When the power demander inputs a confirmation instruction through the power demand terminal, the information confirmation module sends the corresponding power instruction to the execution mechanism, which then executes the power instruction.

[0004] For example, the invention patent with announcement number CN110266899A discloses a method for identifying customer intent and a customer service system, which includes the following steps: S1, when a customer's voice is received, the call center determines the agent voice channel for transmitting the agent's voice corresponding to the customer's voice; S2, during the communication between the agent and the customer, the voice stream platform collects the agent's voice transmitted in the agent voice channel and converts the agent's voice into text information; S3, by matching the text information with a preset intent model, the customer intent contained in the text information is identified; S4, the business system database obtains the response information that matches the customer intent and sends the response information to the agent platform; S5, the agent platform displays the response information on its own display interface.

[0005] The above-disclosed technical solutions have at least the following technical problems: they rely on text matching models to identify customer intent, but do not consider the integration of risk data, making it difficult to accurately capture customers' real needs in complex contexts. They also lack sentiment analysis and dynamic risk assessment mechanisms, and cannot identify and respond to risk events that cause changes in customer emotions in real time.

[0006] To address the above problems, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent control optimization method and system for power customer service risks. By combining power knowledge graphs, dynamic risk field models, and interactive risk monitoring models, it achieves comprehensive monitoring, accurate evaluation, and dynamic control of the customer interaction process, and automatically generates intervention scripts that match the risk level. This solves the problems of lacking sentiment analysis and dynamic risk assessment mechanisms in complex contexts, and the inability to identify and respond to changes in customer emotions in real time.

[0008] To achieve the above objectives, the present invention provides the following technical solution: acquiring real-time user risk data, processing the risk data through a pre-built dynamic risk field model to generate risk mutation signals and optimal intervention paths; responding to risk mutation signals by calling a risk semantic unit library to generate corresponding intervention scripts; based on the optimal intervention path, pushing the intervention scripts to the call center agent's terminal through a cloud assistant; collecting feedback results from the call center agent after executing the intervention scripts, and optimizing the knowledge graph structure.

[0009] In a preferred embodiment, the method for acquiring the risk data specifically includes: inputting the acquired customer service call audio into a speech recognition engine to generate interactive text, and extracting semantic feature vectors from the interactive text; inputting the semantic feature vectors into a knowledge graph module to generate user intent vectors; and inputting the semantic feature vectors and user intent vectors together into a preset interactive risk monitoring model to output a risk score.

[0010] In a preferred embodiment, the dynamic risk field model includes a risk data Riemannian manifold, a risk propagation dynamics equation, a risk cost function, and an affine connection.

[0011] In a preferred embodiment, the step of processing risk data through a pre-constructed dynamic risk field model to generate risk mutation signals and optimal intervention paths specifically involves: constructing a multi-dimensional risk data space based on risk data parameters; constructing a risk data Riemannian manifold by combining a preset manifold metric structure and curvature upper limit constraint; calculating the relative positional changes between state points on the Riemannian manifold through manifold connectivity and describing the evolution of risk states over time based on the risk propagation dynamics equation; calculating the manifold curvature tensor based on connectivity and quantifying the degree of spatial curvature in a specific direction through cross-sectional curvature calculation; generating a risk mutation signal when the cross-sectional curvature value exceeds a preset risk trigger threshold; predicting the risk evolution path through manifold geodesics calculation in response to the risk mutation signal; and determining the optimal intervention path in the predicted path with the goal of minimizing a preset risk cost function.

[0012] In a preferred embodiment, the step of calling the risk semantic unit library to generate the corresponding intervention script specifically involves: constructing a risk semantic unit library based on domain expert knowledge; inputting the real-time acquired risk critical state vector into the risk semantic unit library; calculating the semantic correlation between each semantic unit and the risk critical state vector, and selecting the target semantic unit with the highest correlation; based on the semantic unit with the highest correlation, combined with the pre-generated optimal intervention path parameters, selecting an intervention script template that matches the target semantic unit, and generating the final intervention script.

[0013] In a preferred embodiment, the step of inputting the acquired customer service call audio into a speech recognition engine to generate interactive text and extracting semantic feature vectors from the interactive text specifically involves: real-time acquisition of the audio stream of the dialogue between customer service and customer; using automatic speech recognition technology to convert the audio stream of the dialogue into interactive text; separating the roles of customer service and customer in the interactive text based on voiceprint features to obtain role text; and performing semantic parsing on the role text to generate a semantic feature vector containing demand identification, key entity information, and sentiment analysis data.

[0014] In a preferred embodiment, the step of inputting the semantic feature vector and the user intent vector into a preset interaction risk monitoring model and outputting a risk score specifically involves: inputting the semantic feature vector and the user intent vector into the interaction risk monitoring model to calculate the risk score; and generating a corresponding risk level identifier through the output unit based on the risk score calculation result.

[0015] In a preferred embodiment, the step of pushing intervention scripts to call center terminals via a cloud assistant based on the optimal intervention path specifically involves selecting an intervention script template according to the optimal intervention path and pushing it to the frontline call center terminals.

[0016] In a preferred embodiment, the step of collecting feedback results from call center agents after performing intervention calls and optimizing the knowledge graph structure specifically involves: recording response time data and intervention result data during the service process, constructing an intervention effectiveness evaluation rule set; and adjusting and pruning redundant execution nodes of the knowledge graph based on the output results of the evaluation rule set.

[0017] A system for intelligent control and optimization of power customer service risks includes: a module for acquiring real-time user risk data, processing the risk data through a pre-built dynamic risk field model, generating risk mutation signals and optimal intervention paths; a script generation module for responding to risk mutation signals, calling a risk semantic unit library to generate corresponding intervention scripts, and pushing the intervention scripts to the operator's terminal via a cloud assistant based on the optimal intervention path; and an optimization module for collecting feedback results from operators after executing intervention scripts and optimizing the knowledge graph structure.

[0018] The technical effects and advantages of the intelligent control optimization method and system for power customer service risks of this invention are as follows: This invention innovatively maps customer semantic risk data into a high-dimensional space with manifold metrics and curvature constraints by constructing a risk field model that integrates Riemannian geometry and dynamic risk propagation mechanisms. Combined with affine connections and geodesic modeling, it achieves continuous characterization and trend prediction of customer risk status over time. Furthermore, the system introduces a risk propagation dynamic equation and a risk cost function optimization mechanism to generate globally optimal intervention paths driven by risk mutation signals, and intelligently matches script templates using a semantic unit library. Compared to traditional rule-based or classification-based risk control models, this method more realistically characterizes the nonlinear relationships between semantic features, anticipates risk mutation trends, and improves the timeliness and accuracy of intervention responses. Simultaneously, it optimizes the knowledge graph structure through a feedback mechanism, enabling dynamic evolution and intelligent control of the power customer service system. Attached Figure Description

[0019] Figure 1 A schematic diagram of the intelligent control optimization method for power customer service risks provided by the present invention.

[0020] Figure 2 A schematic diagram of the system structure of the intelligent control optimization method for power customer service risks provided by the present invention.

[0021] Figure 3 A schematic diagram of the Riemannian manifold for risk data provided in this invention.

[0022] Figure 4 The cross-sectional curvature diagram provided for this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 This invention presents an intelligent control optimization method for power customer service risks, comprising the following steps: S1: Acquire real-time user risk data, process the risk data through a pre-built dynamic risk field model, and generate risk mutation signals and optimal intervention paths; S2, in response to risk mutation signals, calls the risk semantic unit library to generate corresponding intervention statements; S3, based on the optimal intervention path, pushes the intervention script to the call center agent's terminal through a cloud assistant; S4 collects feedback results from call center agents after they perform interventions, and optimizes the knowledge graph structure.

[0025] This embodiment innovatively maps customer semantic risk data into a high-dimensional space with manifold metrics and curvature constraints by constructing a risk field model with Riemannian geometry and a dynamic risk propagation mechanism. Combined with affine connections and geodesic modeling, it achieves continuous characterization and trend prediction of customer risk status over time. Furthermore, the system introduces a risk propagation dynamic equation and a risk cost function optimization mechanism to generate globally optimal intervention paths driven by risk mutation signals, and intelligently matches script templates using a semantic unit library. Compared to traditional rule-based or classification-based risk control models, this method can more realistically characterize the nonlinear relationships between semantic features, perceive risk mutation trends in advance, and improve the timeliness and accuracy of intervention responses. Simultaneously, it optimizes the knowledge graph structure through a feedback mechanism, enabling dynamic evolution and intelligent control of the power customer service system.

[0026] S1 acquires real-time user risk data, processes the risk data through a pre-built dynamic risk field model, and generates risk mutation signals and optimal intervention paths.

[0027] In this embodiment, the method for obtaining the risk data is specifically as follows: The acquired customer service call audio is input into the speech recognition engine to generate interactive text, and semantic feature vectors are extracted from the interactive text. Input the semantic feature vector into the knowledge graph module to generate the user intent vector; The semantic feature vector and the user intent vector are input into a pre-defined interaction risk monitoring model to output a risk score.

[0028] In this embodiment, the step of inputting the acquired customer service call audio into the speech recognition engine to generate interactive text, and extracting semantic feature vectors from the interactive text, specifically involves: Real-time acquisition of audio streams of conversations between customer service representatives and customers; Automatic speech recognition technology is used to convert dialogue audio streams into interactive text; Based on voiceprint features, customer service and customer roles are separated in interactive text to obtain role text; The character text is semantically parsed to generate a semantic feature vector containing demand identification, key entity information, and sentiment analysis data.

[0029] In this embodiment, the audio stream of the conversation between the customer manager, the on-site service personnel of the power supply station and the customer is collected in real time through wearable audio pickup device and fixed audio pickup device at the counter. The audio stream is then processed for noise reduction and transmitted to the data processing backend in real time. In the data processing backend, automatic speech recognition technology is used to convert audio streams into interactive text, removing noise, silent segments and irrelevant information. Separate the voiceprint features of customer service representatives and customers from the interactive text to obtain the character text; Based on NLP technology, semantic parsing of user role text is performed to identify key information in customer dialogues and extract semantic feature vectors of potential complaints and dissatisfaction.

[0030] In this embodiment, the semantic feature vector is input into the knowledge graph module to generate a user intent vector.

[0031] In this embodiment, a knowledge graph structure is defined based on the domain of electricity customer service. The semantic feature vector is input into the knowledge graph module, and the specific expression is as follows:

[0032]

[0033] In the formula, User role text semantic feature vectors; To make the text Convert it into a vector representation; This is a weighted embedding vector for a knowledge graph, representing comprehensive information about entities and relationships within the knowledge graph. For relationship The weights; For entities The embedding vector represents an entity in the knowledge graph; For relationship The embedding vector represents the relationship between entities; For entities The embedding vector represents another entity in the knowledge graph.

[0034] In this embodiment, the final user intent vector is generated based on the weighted embedding vector of the knowledge graph, and the specific expression is as follows:

[0035] In the formula, This is the final generated user intent vector; semantic feature vector The weight, Knowledge graph vectors The weight, ; This is a weighted embedding vector based on a knowledge graph.

[0036] In this embodiment, the step of inputting the semantic feature vector and the user intent vector into a preset interaction risk monitoring model and outputting a risk score is specifically as follows: The semantic feature vector and the user intent vector are input into the interaction risk monitoring model to calculate the risk score; Based on the risk score calculation results, the corresponding risk level identifier is generated through the output unit.

[0037] In this embodiment, a risk assessment feature vector is constructed based on semantic feature vectors and user intent vectors, combined with auxiliary modal features; The risk assessment feature vector is input into the interactive risk monitoring model, and the risk score calculation result is obtained through the constructed risk score calculation formula. Based on the risk score calculation results, the corresponding risk level identifier is generated through the output unit.

[0038] The specific formula for calculating the input feature vector for risk assessment is as follows:

[0039] In the formula, To construct the risk assessment feature vector, For user intent vectors, For auxiliary modal features, This is the semantic feature vector of the user's call.

[0040] The specific formula for calculating the risk score is as follows:

[0041] In the formula, The risk score calculation result has a value range of [0,1]. Input risk assessment feature vector Interactive risk monitoring model; , This is the weight matrix of the model; , For bias terms; It is an activation function; It is the sigmoid activation function.

[0042] Risk level indicators are as follows:

[0043] in, For discrete risk level labels, , Thresholds for risk level classification, meeting the following conditions .

[0044] In this embodiment, the dynamic risk field model includes a risk data Riemannian manifold, a risk propagation dynamics equation, a risk cost function, and an affine connection.

[0045] It should be noted that the Riemannian manifold for risk data defines the geometric properties of the risk data space, characterizes the curvature and distance of the manifold, and forms the basic model for the risk space; Affine communication utilizes the geometry of Riemannian manifolds to calculate the relative changes of risk data on the manifold, providing dynamic information between points on the manifold and playing a role in subsequent risk propagation dynamics equations; The risk propagation dynamics equation describes the temporal evolution of risk data through affine connections and calculates the development trend of risk data in the risk space. The risk cost function, based on current risk evolution predictions, helps to find the optimal intervention path by minimizing risk exposure and intervention costs, in order to reduce future risk exposure and losses.

[0046] In this embodiment, the process of processing risk data through a pre-constructed dynamic risk field model to generate risk mutation signals and optimal intervention paths specifically includes: A multidimensional risk data space is constructed based on risk data parameters, and a risk data Riemannian manifold is constructed by combining a preset manifold metric structure and curvature upper limit constraint. On a Riemannian manifold, the relative positional changes between state points are calculated through manifold connectivity, and the evolution of risk states over time is described based on the risk propagation dynamics equation. The manifold curvature tensor is calculated based on the connection relationship, and the degree of spatial curvature in a specific direction is quantified by cross-sectional curvature calculation. When the cross-sectional curvature value exceeds the preset risk trigger threshold, a risk mutation signal is generated; In response to risk mutation signals, the risk evolution path is predicted by calculating manifold geodesics; With the goal of minimizing the preset risk cost function, the optimal intervention path is determined from the predicted path.

[0047] Figure 3 A schematic diagram of the Riemannian manifold of the risk data provided in this embodiment.

[0048] Figure 4 The cross-sectional curvature diagram provided for this embodiment.

[0049] It should be noted that the risk data parameters include the extracted semantic feature vector, the user intent vector generated from the knowledge graph, and the risk score generated from the interaction risk monitoring model; the manifold connection relationship is an affine connection.

[0050] In this embodiment, a risk data space is defined. This space describes the risk data of customers during the interaction process. Each customer interaction state is represented by a three-dimensional vector, including a risk score. User intent vector and semantic feature vector .

[0051] The specific formula for defining the Riemannian manifold of risk data is as follows:

[0052] In the formula, For Riemannian metric tensors, used to describe distance metrics in risky manifolds; The upper bound of the cross-sectional curvature is preset. To control the local curvature of the risk field; For coordinate differential tensor product, it represents the metric of infinitesimal displacement on the manifold; Let be the high-dimensional space of the real number field in which the entire state vector resides. It is the vector dimension.

[0053] Riemannian metric tensor The specific calculation formula is as follows:

[0054] In the formula, Kronecker delta function, representing the identity matrix, i.e. ; This represents the risk gradient weight coefficient, which controls the degree of influence of changes in risk scores on the manifold. , Indicates risk Regarding state variables and The partial derivatives; , representing the intention gradient weight coefficient, which controls the influence of intention information on the manifold structure; The first in the user intent vector One portion, User intent vector The dimension; , They represent the first The intention component is related to the first and the The rate of change of each state variable.

[0055] In this embodiment, affine connections are defined to describe the relative positional changes between points on the manifold.

[0056] The specific formula for defining affine connections is as follows:

[0057] In the formula, Let be the affine connection coefficient, representing the in the manifold. and the When a change occurs in the direction, along the The changing trend in each direction reflects the "bending" or change of the coordinate system; Riemannian metric tensor The inverse matrix; For metric components coordinates The partial derivatives, For metric components coordinates The partial derivatives, For metric components coordinates The partial derivatives of .

[0058] In this embodiment, a risk propagation dynamics equation is constructed to calculate the evolution of risk state over time.

[0059] The specific formula for the risk propagation dynamics equation is as follows:

[0060] In the formula, The covariant derivative in the time direction ensures the geometric invariance of the evolution process on the manifold; This is a vector of the rate of change of risk data; The rate of change vector Partial derivative with respect to time; , For vectors The amount; These are the coordinate basis vectors; For affine connection coefficients.

[0061] In this embodiment, the Riemannian manifold curvature tensor is calculated based on the affine connection.

[0062] The formula for the Riemannian manifold curvature tensor is as follows:

[0063] In the formula, Let Riemannian manifold curvature tensor be the standard tensor. , , , , , These are the components of the affine connection coefficient, used to calculate curvature.

[0064] In this embodiment, the local risk curvature is quantified by calculating the cross-sectional curvature.

[0065] The formula for the curvature of a cross section is as follows:

[0066] In the formula, Let be the scalar of cross-sectional curvature, representing the tangent plane (by...). , The degree of local bending of Zhang Cheng; , For any pair of orthogonal vectors in the tangent space, they must satisfy... ; For the covariant form of the Riemann curvature tensor, order reduction is achieved through metric reduction: .

[0067] In this embodiment, a risk trigger threshold is set. When the absolute value of the cross-sectional curvature exceeds the threshold, a risk event is triggered, risk information is generated, and the risk data coordinates at the trigger time are recorded.

[0068] The formula for setting the risk trigger threshold is as follows:

[0069] In the formula, This is the cross-sectional curvature threshold; a risk event is triggered when the absolute value of the curvature exceeds this value.

[0070] This is the risk critical state vector, which records the risk data coordinates at the trigger moment.

[0071] In this embodiment, when a risk mutation signal occurs, the natural evolution of the risk data on the manifold is described by the geodesic equation based on the risk data coordinates at the trigger time.

[0072] The geodesic equations are as follows:

[0073] In the formula, For risk data coordinates, For time parameters, For affine connection coefficients, Let be the derivative of the state coordinates with respect to time. This is the component of external risk disturbance forces.

[0074] In this embodiment, among all possible risk data paths, the path that minimizes the total path "cost" is determined by minimizing the risk cost function; that is, the optimal intervention path.

[0075] The specific expression for minimizing the risk cost function is as follows:

[0076] In the formula, The dot product of metric and velocity ( ), representing the square of the path length; For arc length infinitesimals on the manifold (minimize path length); For real-time risk scoring functions along the path; , is the regularization parameter, which balances the weights of path length and risk exposure; , These represent the start and end times of the evolutionary path.

[0077] S2, in response to risk mutation signals, calls the risk semantic unit library to generate corresponding intervention statements.

[0078] In this embodiment, the step of calling the risk semantic unit library to generate the corresponding intervention script specifically involves: A risk semantic unit library is constructed based on domain expert knowledge; Input the real-time acquired risk critical state vector into the risk semantic unit library; Calculate the semantic correlation between each semantic unit and the risk critical state vector, and select the target semantic unit with the highest correlation. Based on the semantic unit with the highest relevance, and combined with the pre-generated optimal intervention path parameters, an intervention script template matching the target semantic unit is selected to generate the final intervention script.

[0079] In this embodiment, a risk semantic unit library is constructed based on domain expert knowledge, and the risk critical state vectors acquired in real time are input into the risk semantic unit library to calculate the semantic relevance.

[0080] The formula for calculating semantic relevance is as follows:

[0081] In the formula, As a semantic unit, This represents the risk critical state vector; This represents a linear correlation between semantics and state; is the Euclidean length of the semantic unit. is the Euclidean length of the critical state vector; Let cosine be the angle between semantics and state. To standardize the similarity value.

[0082] In this embodiment, the target semantic unit with the highest semantic relevance is selected based on the calculation of semantic relevance. The specific expression for the optimal semantic unit is as follows:

[0083] In the formula, For optimal matching semantic unit, To standardize the similarity values, A predefined set of risk semantics.

[0084] S3, based on the optimal intervention path, pushes intervention scripts to the call center terminal via a cloud assistant.

[0085] In this embodiment, the step of pushing the intervention script to the operator's terminal via a cloud assistant based on the optimal intervention path specifically involves: Based on the optimal intervention path, select the intervention script template and push it to the front-line call center operators' terminals.

[0086] It should be noted that the script templates are differentiated by the weight of the optimal path, and the script delivery method is adjusted according to the risk level.

[0087] In this embodiment, the risk weight of the optimal path is used. Choose an appropriate intervention script template The specific regular expression is as follows:

[0088] In the formula, For appropriate intervention script templates, The optimal semantic unit. The risk weight is the optimal path.

[0089] In this embodiment, the optimal semantic unit Injection template In the process, the final intervention script is obtained. The specific expression is as follows:

[0090] That is, the content of the script is a combination of the selected template and the reasons for the risk.

[0091] In this embodiment, the risk levels are classified based on the risk scoring results. Different script delivery strategies are set up, and the specific strategies for different levels of script delivery are shown in the table below: Table 1

[0092] The generated intervention script is uploaded to the cloud assistant task scheduling module, and then pushed to the designated front-line call center terminal via the authentication and routing module, where it is automatically displayed through an interface call.

[0093] S4 collects feedback results from call center agents after they perform interventions, and optimizes the knowledge graph structure.

[0094] In this embodiment, the step of collecting feedback results from call center agents after performing intervention calls and optimizing the knowledge graph structure specifically involves: Record response time data and intervention result data during the service process, and construct a set of rules for evaluating intervention effectiveness; Based on the output of the evaluation rule set, the knowledge graph is adjusted and redundant pruning is performed.

[0095] It should be noted that the feedback results refer to multi-dimensional indicators covering customer risk level reduction, response timeliness, and customer feedback satisfaction.

[0096] In this embodiment, the system performs real-time data collection and structured recording of the entire customer service process, including multi-dimensional indicators such as customer risk level reduction, response timeliness, and customer feedback satisfaction. Based on the collected results, a set of intervention effectiveness evaluation rules is constructed. According to the requirements of the intervention effectiveness evaluation rule set, specific indicators of intervention effect are calculated through intervention evaluation functions. Based on the output results of the intervention evaluation functions, nodes in the high-frequency and low-efficiency service node set are optimized.

[0097] In this embodiment, the service session number is defined as The specific formula for the intervention effectiveness evaluation function of this session is as follows:

[0098] In the formula, The intervention effect index is calculated for the intervention function. The risk level reduction value for the client. and These represent the customer risk levels assessed by the system before and after the intervention. To score for response timeliness, To provide customer satisfaction feedback, , , The weighting coefficients for the three types of indicators are given by the following weighting constraints: Through the intervention evaluation function, the system can identify which service processes are performing well and which have problems such as response delays or ineffective interventions.

[0099] In this embodiment, based on the results of the intervention evaluation function, a set of high-frequency and inefficient service nodes is statistically analyzed. The specific formula for defining this set is as follows:

[0100] In the formula, For a set of high-frequency, low-efficiency service nodes, For service nodes Frequency of being called in the most recent period For nodes The average intervention assessment score of the corresponding service process As a frequency threshold, The threshold for effectiveness.

[0101] For nodes with a high frequency and low efficiency service concentration, the system performs optimization through node adjustment and redundancy reduction: Node adjustment: By reducing the weights of the edges connecting these high-frequency, inefficient nodes to other entities, their influence in the system is reduced. This is achieved by modifying the edge weights in the knowledge graph, as shown in the following formula:

[0102] In the formula, For the optimized edge weights, These are the original edge weights; , The adjustment factor represents the magnitude of the reduction in edge weights.

[0103] Redundancy pruning: Euclidean distance is used to determine whether there is a redundant relationship between two nodes, and redundant edges are pruned. The specific steps are as follows: The Euclidean distance calculation formula is as follows:

[0104] In the formula, and Representing nodes respectively and In the Dimension value, For nodes and The Euclidean distance between them.

[0105] Based on the results of the Euclidean distance, we define a redundancy check, the specific formula of which is as follows:

[0106] In the formula, For nodes and The Euclidean distance between them The preset threshold indicates that when the Euclidean distance is less than this threshold, it is considered... and The relationships between them are redundant.

[0107] The specific formula for the cropping operation is as follows:

[0108] In the formula, The original knowledge graph contains nodes and edges. The trimmed knowledge graph For nodes and The edges between them.

[0109] Example 2, Figure 2 The present invention provides a system for optimizing intelligent control of power customer service risks, comprising: The risk field model construction module is used to acquire real-time user risk data, process the risk data through a pre-built dynamic risk field model, and generate risk mutation signals and optimal intervention paths. The script generation module is used to respond to risk mutation signals, call the risk semantic unit library to generate corresponding intervention scripts, and push the intervention scripts to the call center terminal through the cloud assistant based on the optimal intervention path. The optimization module is used to collect feedback results from call center operators after they perform interventions and to optimize the knowledge graph structure.

[0110] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0111] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0112] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0113] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0114] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0115] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control and optimization method for power customer service risks, characterized in that, Includes the following steps: Acquire real-time user risk data, process the risk data through a pre-built dynamic risk field model, and generate risk mutation signals and optimal intervention paths; In response to risk mutation signals, the risk semantic unit library is invoked to generate corresponding intervention statements; Based on the optimal intervention path, the intervention script is pushed to the call center terminal via a cloud assistant; Collect feedback results from call center operators after they perform interventions, and optimize the knowledge graph structure.

2. The intelligent control optimization method for power customer service risk according to claim 1, characterized in that, The method for obtaining the risk data is as follows: The acquired customer service call audio is input into the speech recognition engine to generate interactive text, and semantic feature vectors are extracted from the interactive text. Input the semantic feature vector into the knowledge graph module to generate the user intent vector; The semantic feature vector and the user intent vector are input into a pre-defined interaction risk monitoring model to output a risk score.

3. The intelligent control optimization method for power customer service risk according to claim 2, characterized in that, The dynamic risk field model includes the risk data Riemannian manifold, the risk propagation dynamics equation, the risk cost function, and the affine connection.

4. The intelligent control optimization method for power customer service risk according to claim 3, characterized in that, The process of processing risk data through a pre-constructed dynamic risk field model to generate risk mutation signals and optimal intervention paths specifically involves: A multidimensional risk data space is constructed based on risk data parameters, and a risk data Riemannian manifold is constructed by combining a preset manifold metric structure and curvature upper limit constraint. On a Riemannian manifold, the relative positional changes between state points are calculated through manifold connectivity, and the evolution of risk states over time is described based on the risk propagation dynamics equation. The manifold curvature tensor is calculated based on the connection relationship, and the degree of spatial curvature in a specific direction is quantified by cross-sectional curvature calculation. When the cross-sectional curvature value exceeds the preset risk trigger threshold, a risk mutation signal is generated; In response to risk mutation signals, the risk evolution path is predicted by calculating manifold geodesics; With the goal of minimizing the preset risk cost function, the optimal intervention path is determined from the predicted path.

5. The intelligent control optimization method for power customer service risk according to claim 4, characterized in that, The process of calling the risk semantic unit library to generate the corresponding intervention script is as follows: A risk semantic unit library is constructed based on domain expert knowledge; Input the real-time acquired risk critical state vector into the risk semantic unit library; Calculate the semantic correlation between each semantic unit and the risk critical state vector, and select the target semantic unit with the highest correlation. Based on the semantic unit with the highest relevance, and combined with the pre-generated optimal intervention path parameters, an intervention script template matching the target semantic unit is selected to generate the final intervention script.

6. The intelligent control optimization method for power customer service risk according to claim 5, characterized in that, The process involves inputting the acquired customer service call audio into a speech recognition engine to generate interactive text, and then extracting semantic feature vectors from the interactive text. Specifically: Real-time acquisition of audio streams of conversations between customer service representatives and customers; Automatic speech recognition technology is used to convert dialogue audio streams into interactive text; Based on voiceprint features, customer service and customer roles are separated in interactive text to obtain role text; The character text is semantically parsed to generate a semantic feature vector containing demand identification, key entity information, and sentiment analysis data.

7. The intelligent control optimization method for power customer service risk according to claim 6, characterized in that, The step of inputting semantic feature vectors and user intent vectors into a preset interaction risk monitoring model and outputting a risk score is as follows: The semantic feature vector and the user intent vector are input into the interaction risk monitoring model to calculate the risk score; Based on the risk score calculation results, the corresponding risk level identifier is generated through the output unit.

8. The intelligent control optimization method for power customer service risk according to claim 7, characterized in that, The method of pushing intervention scripts to call center agents' terminals via a cloud assistant based on the optimal intervention path is as follows: Based on the optimal intervention path, select the intervention script template and push it to the front-line call center operators' terminals.

9. The intelligent control optimization method for power customer service risk according to claim 8, characterized in that, The process of collecting feedback results from call center agents after they perform intervention calls and optimizing the knowledge graph structure involves the following steps: Record response time data and intervention result data during the service process, and construct a set of rules for evaluating intervention effectiveness; Based on the output of the evaluation rule set, the knowledge graph is adjusted and redundant pruning is performed.

10. A system using the intelligent control optimization method for power customer service risk as described in any one of claims 1-9, comprising: The risk field model construction module is used to acquire real-time user risk data, process the risk data through a pre-built dynamic risk field model, and generate risk mutation signals and optimal intervention paths. The script generation module is used to respond to risk mutation signals, call the risk semantic unit library to generate corresponding intervention scripts, and push the intervention scripts to the call center terminal through the cloud assistant based on the optimal intervention path. The optimization module is used to collect feedback results from call center operators after they perform interventions and to optimize the knowledge graph structure.

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