Power grid customer intelligent service robot service quality monitoring system

Abstract

The invention discloses a power grid customer intelligent service robot service quality monitoring system, and belongs to the technical field of service monitoring analysis. The method is used for solving the technical problems that in an existing scheme, the local service quality targeted supervision effect of different service functions of a service robot is poor, and the differentiated management effect is poor. The method comprises the following steps: monitoring different service functions of a power grid customer intelligent service robot in the aspect of function quality and carrying out data processing to obtain function supervision sequences corresponding to the different service functions, and carrying out data analysis in a transverse dimension and a longitudinal dimension on the function supervision sequences corresponding to the different service functions; the analysis results of different dimensions are utilized to carry out overall analysis and dynamic marking of corresponding local service states on different service functions, and multi-dimensional processing analysis with reliable function content management is carried out on self-service contents corresponding to all marked service functions; and according to analysis results of different dimensions, targeted function content management prompts are actively carried out on different service functions.

Description

Technical Field

[0001] This invention relates to the field of service monitoring and analysis technology, and specifically to a service quality monitoring system for an intelligent service robot for power grid customers. Background Technology

[0002] Power grid customer service robots are intelligent customer service solutions specifically designed for power companies. They aim to improve customer service quality, efficiency, and user experience through automation. These robots are typically based on artificial intelligence technologies, including natural language processing (NLP) and machine learning, and are able to understand and respond to customer inquiries or requests.

[0003] The existing intelligent service robot service quality monitoring scheme for power grid customers cannot be implemented based on the existing power grid service function modules to carry out modular supervision and processing analysis, and cannot implement targeted service quality processing and management for different power grid service functions based on the analysis results. This results in poor targeted supervision and differentiated management of the local service quality of different service functions of the service robot. Summary of the Invention

[0004] The purpose of this invention is to provide a service quality monitoring system for intelligent service robots for power grid customers, which solves the technical problems of poor targeted supervision and differentiated management of local service quality of different service functions of service robots in existing solutions.

[0005] The objective of this invention can be achieved through the following technical solutions: A service quality monitoring system for intelligent service robots for power grid customers, comprising: The service function quality monitoring and processing module is used to monitor and process the function quality of different service functions of the smart service robot for power grid customers, and obtain the function supervision sequence corresponding to different service functions. The service function quality multidimensional analysis module is used to perform horizontal and vertical data analysis based on the function supervision sequence corresponding to different service functions, and to use the analysis results of different dimensions to perform overall analysis and dynamic marking of the corresponding local service status of different service functions to obtain the first service function, the second service function, or the third service function. The Service Function Quality Extension and Integration Module is used to perform reliable multi-dimensional processing and analysis of the self-service content corresponding to all marked service functions, and to proactively implement targeted function content management prompts for different service functions based on the analysis results of different dimensions.

[0006] Preferably, according to a preset regulatory cycle, multi-dimensional data monitoring and statistics are performed on the different service functions of the smart service robot for power grid customers to obtain the total number of service failures and the total number of service transfers corresponding to different service functions; Furthermore, based on the service evaluations corresponding to service non-transfer for different service functions, the total number of satisfied and dissatisfied service non-transfers for different service functions is calculated based on the service evaluations; the functional reliability value corresponding to the service function is calculated based on the total number of satisfied and dissatisfied service non-transfers and the total number of service non-transfers; and the functional coordination value corresponding to the service function is calculated based on the total number of dissatisfied service non-transfers, the total number of service non-transfers, and the total number of service transfers. The obtained functional reliability values ​​and functional coordination values ​​are sorted and combined to obtain the functional supervision sequence corresponding to the service function.

[0007] Preferably, the functional reliability value and functional coordination value in the functional supervision sequence corresponding to different service functions are obtained sequentially. When performing horizontal data analysis on the functional reliability value corresponding to the service function, the functional reliability value is compared with the preset functional reliability standard value for judgment. If the functional reliability value is less than the functional reliability standard value, a functional unreliable label is generated, and the corresponding functional reliability flag is set to 1. Conversely, a functional reliability label is generated, and the corresponding functional reliability flag is set to 0.

[0008] Preferably, when performing vertical dimension data analysis on the functional coordination values ​​corresponding to the service functions, the functional coordination values ​​are compared and judged with preset functional coordination standard values; If the function coordination value is greater than the corresponding function coordination standard value, a "function not rich" label will be generated, and the corresponding "function rich" identifier will be set to 1. Conversely, if the feature-rich tag is not specified, a feature-rich tag will be generated, and the corresponding feature-rich identifier will be set to 0.

[0009] Preferably, the functional reliability identifier and the functional richness identifier are summed, and the result is set as the service supervision value corresponding to the service function. Data analysis is performed on service monitoring values ​​to determine the local service status corresponding to service functions.

[0010] Preferably, if the service supervision value is 0, the service function to which it belongs is marked as the first service function, and a prompt indicating that the first service function is in a normal local service status is given. If the service supervision value is 1, the service function will be marked as the second service function, and a prompt will be given indicating a minor abnormality in the local service status of the second service function. If the service supervision value is 2, the service function will be marked as a third service function, and a prompt will be given indicating a severe local service anomaly for the third service function.

[0011] Preferably, all marked first service functions are traversed, counted, and analyzed. If the total number of first service functions is not 0, the self-service of the power grid customer intelligent service robot is prompted to be partially reliable, and the function content of all second service functions is prompted to be partially managed, and the function content of all third service functions is prompted to be fully managed. If the total number of the first service functions is 0, a message will be displayed indicating that the self-service of the power grid customer intelligent service robot is unreliable, and a message will be displayed indicating that the overall management of the function content of all service functions will be implemented.

[0012] Preferably, the management impact value of all third-party service functions is calculated and obtained; If the management impact value is less than or equal to 0, a prompt will be made indicating that the self-service of the smart service robot for power grid customers is partially reliable, and a prompt will be made indicating that the function content of all second service functions is partially managed, and the function content of all third service functions is managed as a whole. Conversely, it will prompt that the self-service of the smart service robot for power grid customers is unreliable and provide a prompt for overall management of the function content of all service functions.

[0013] Compared to existing solutions, the beneficial effects achieved by this invention are: This invention monitors and processes the functional quality of different service functions of the smart service robot for power grid customers, and obtains the functional supervision sequence corresponding to different service functions. It realizes modular supervision and diversified processing based on existing power grid service function modules, and can provide reliable data support for subsequent quality monitoring and analysis of different dimensions corresponding to different service functions.

[0014] This invention performs horizontal and vertical data analysis on the functional monitoring sequences corresponding to different service functions, and uses the analysis results from different dimensions to perform overall analysis and dynamic marking of the corresponding local service status of different service functions. This enables diversified expansion and utilization of different monitoring and processing data of existing power grid service function modules, and improves the processing and analysis effect of the local service status corresponding to different service functions.

[0015] This invention reliably processes and analyzes the self-service content corresponding to all marked service functions through multi-dimensional functional content management. Based on the analysis results of different dimensions, it proactively implements targeted functional content management prompts for different service functions. This enables further expansion and utilization of monitoring and processing data from different aspects of service functions in the early stages. It allows for targeted service quality processing and management of different power grid service functions, improving the targeted supervision and differentiated management effects of local service quality of different service functions of service robots. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating the operation of a power grid customer intelligent service robot service quality monitoring system according to the present invention.

[0018] Figure 2 This is a flowchart illustrating the process of obtaining the functional regulatory sequence in this invention.

[0019] Figure 3 This is a flowchart illustrating the horizontal dimension data analysis of the functional reliability values ​​corresponding to service functions in this invention.

[0020] Figure 4 This is a flowchart illustrating the vertical dimension data analysis of the functional coordination values ​​corresponding to service functions in this invention.

[0021] Figure 5 This is a flowchart illustrating the data analysis of management impact values ​​in this invention. Detailed Implementation

[0022] 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.

[0023] like Figure 1 As shown, the present invention is a service quality monitoring system for an intelligent service robot for power grid customers, comprising: The service function quality monitoring and processing module is used to monitor and process the functional quality of different service functions of the intelligent service robot for power grid customers, and to obtain the functional supervision sequence corresponding to different service functions; including: According to the preset regulatory cycle, the unit of the regulatory cycle is days, and the specific value is not limited. It can be customized according to the actual application scenario. Multi-dimensional data monitoring and statistics are carried out on the different service functions of the power grid customer intelligent service robot to obtain the total number of service failures and the total number of service transfers corresponding to different service functions. The different service functions of the service robot are determined based on existing service data, and can also be added, deleted, or modified according to the application needs of actual application scenarios; the service functions include, but are not limited to, electricity usage, bill inquiry, and business processing. In addition, service evaluations are generated based on the service functions corresponding to the non-transfer of services. These evaluations are obtained from the user's choices after the consultation ends and include both satisfaction and dissatisfaction. The total number of satisfied and dissatisfying non-transfer services for each service function is calculated based on these evaluations. Service transfer refers to transferring the service robot's service to human service, indicating that the service robot cannot resolve the user's service needs. like Figure 2 As shown, the functional reliability value corresponding to the service function is calculated based on the total number of satisfied calls without service transfer and the total number of times the service was not transferred. The functional coordination value corresponding to the service function is calculated based on the total number of dissatisfied calls without service transfer, the total number of times the service was not transferred, and the total number of times the service was transferred. The functional reliability value is expressed by the formula. The calculation yields the following result: GK represents the functional reliability value; NM and N1 represent the total number of satisfactory service calls not transferred and the total number of service calls not transferred, respectively. Functional harmonization values ​​are obtained through the formula The calculation yields the result; where GX is the functional coordination value; NZ and N2 are the total number of service transfers and the total number of services, respectively. In addition, data processing and analysis can be performed using functional reliability models and functional coordination models respectively, and the corresponding analysis output values ​​can be set as functional reliability values ​​and functional coordination values ​​respectively. Both the functional reliability model and the functional coordination model are based on artificial intelligence models, specifically including: Obtain standard training data; the standard training data includes standard input data with attributes consistent with the total number of satisfied service calls not transferred, the total number of service calls not transferred, and the total number of dissatisfied service calls not transferred, as well as standard output data representing the functional reliability type and the functional coordination type. Artificial intelligence models are constructed by training on training data with different standards, and after training on artificial intelligence models with different standards, they are respectively labeled as functionally reliable models and functionally consistent models; the artificial intelligence models include BP neural network models or RBF neural network models. The method for processing and obtaining functional reliability values ​​and functional coordination values ​​is not limited and can be chosen independently according to the application requirements of the actual application scenario. It should be noted that the functional reliability value and functional coordination value in the embodiments of the present invention are used to process and calculate the regulatory data corresponding to different aspects of the service function, so as to digitally represent the status of the service function corresponding to different aspects, and can provide reliable digital data support for subsequent data expansion analysis of different service functions corresponding to different dimensions. The obtained functional reliability values ​​and functional coordination values ​​are sorted and combined to obtain the functional supervision sequence corresponding to the service function. In this embodiment of the invention, by monitoring and processing the functional quality of different service functions of the smart service robot for power grid customers, a functional supervision sequence corresponding to different service functions is obtained. This realizes modular supervision and diversified processing based on existing power grid service function modules, and can provide reliable data support for subsequent quality monitoring and analysis of different dimensions corresponding to different service functions.

[0024] The service function quality multidimensional analysis module is used to perform horizontal and vertical data analysis based on the functional supervision sequence corresponding to different service functions. It then utilizes the analysis results from different dimensions to perform overall analysis and dynamic labeling of the corresponding local service status of different service functions, resulting in the first, second, or third service function. This includes: like Figure 3 As shown, the functional reliability value and functional coordination value in the functional supervision sequence corresponding to different service functions are obtained in sequence. When performing horizontal data analysis on the functional reliability value corresponding to the service function, the functional reliability value is compared with the preset functional reliability standard value. The specific value of the functional reliability standard value is not limited. It can be determined based on the design requirements data of the actual operation of the service function, or it can be determined based on the median of all functional reliability values ​​corresponding to several historical supervision cycles. If the functional reliability value is less than the functional reliability standard value, a functional unreliable label is generated, and the corresponding functional reliability flag is set to 1. Conversely, a functional reliability label is generated, and the corresponding functional reliability flag is set to 0; like Figure 4 As shown, when performing vertical data analysis on the functional coordination values ​​corresponding to service functions, the functional coordination values ​​are compared with the preset functional coordination standard values. The specific value of the functional coordination standard value is not limited. It can be determined based on the design requirements data of the actual operation of the service function, or it can be determined based on the median of all functional coordination values ​​corresponding to several historical regulatory cycles. If the function coordination value is greater than the corresponding function coordination standard value, a "function not rich" label will be generated, and the corresponding "function rich" identifier will be set to 1. Conversely, if the feature is not specified, a feature-rich tag is generated and the corresponding feature-rich identifier is set to 0. In this embodiment of the invention, by analyzing and digitizing the data of different aspects of the early supervision and processing corresponding to different service functions, it is possible to obtain the functional status of different service functions in different dimensions, and also to provide reliable processing data support for the extended analysis of the local service status corresponding to different service functions. The functional reliability indicator and the functional richness indicator are summed, and the result is set as the service supervision value corresponding to the service function. Analyze service monitoring values ​​to determine the local service status corresponding to service functions; If the service supervision value is 0, the service function to which it belongs will be marked as the first service function, and a prompt will be given indicating that the first service function is in normal local service status. If the service supervision value is 1, the service function will be marked as the second service function, and a prompt will be given indicating a minor abnormality in the local service status of the second service function. If the service supervision value is 2, the service function will be marked as the third service function, and a prompt will be given for the third service function indicating a severe local service anomaly. In this embodiment of the invention, by performing horizontal and vertical data analysis on the functional regulatory sequences corresponding to different service functions, and using the analysis results of different dimensions to perform overall analysis and dynamic marking of the corresponding local service status of different service functions, the invention achieves diversified expansion and utilization of different monitoring and processing data of existing power grid service function modules, thereby improving the processing and analysis effect of the local service status corresponding to different service functions.

[0025] The service function quality expansion and integration module is used to perform reliable multi-dimensional processing and analysis of the self-service content corresponding to all marked service functions, and proactively implement targeted function content management prompts for different service functions based on the analysis results of different dimensions; including: It should be noted that, unlike existing technical solutions that only use a single technical means to analyze and manage data for different service functions, resulting in poor reliability and comprehensiveness in the implementation of the technical means; the embodiments of this invention use different technical means to achieve this, which can effectively improve the reliability and comprehensiveness of the quality assessment and management of the functional content of different service functions. Technical means 1: Traverse, count and analyze all marked first service functions. If the total number of first service functions is not 0, prompt that the self-service of the power grid customer intelligent service robot is partially reliable, and prompt that the function content of all second service functions is partially managed, and prompt that the function content of all third service functions is comprehensively managed. If the total number of the first service functions is 0, a message will be sent indicating that the self-service of the smart service robot for power grid customers is unreliable, and a message will be sent indicating that the function content of all service functions will be managed as a whole. Technical method two: through formula Calculate the management impact value GY of all third service functions; where NQ1 and NQ2 are the total number of all second service functions and the total number of all third service functions, respectively; N3 is the total number of all marked service functions; K1 and K2 are the first and second management impact standard values, respectively. The specific values ​​are not limited and can be determined based on the design requirements data of the actual operation of the service robot or by professionals in the field based on their work experience. The value range is (0, 1); max() represents obtaining the maximum value among several real numbers. In addition, the management impact value can also be obtained by data analysis based on the trained management impact model. The data training and data analysis of the management impact model are the same as those of the functional reliability model and functional coordination model in the embodiments of the present invention. The specific steps are not described here. It is worth noting that, in the embodiments of the present invention, different technical means are used to conduct diversified service quality supervision and analysis on different service functions of service robots, thereby effectively improving the pertinence and reliability of the quality management of local service functions and overall service functions of service robots; like Figure 5 As shown, if the management impact value is less than or equal to 0, the system will prompt that the self-service of the smart service robot for power grid customers is partially reliable, and will also prompt that the function content of all second service functions is partially managed, and the function content of all third service functions is managed as a whole. Conversely, it will prompt that the self-service of the smart service robot for power grid customers is unreliable and provide a prompt for overall management of the function content of all service functions; The two types of management are: partial management of functional content, which involves adding, deleting, or modifying the existing service content of the service function; and overall management of functional content, which involves adding, deleting, or modifying the existing service content and service rules of the service function.

[0026] In this embodiment of the invention, by performing reliable multi-dimensional processing and analysis of the self-service content corresponding to all marked service functions, and by proactively implementing targeted functional content management prompts for different service functions based on the analysis results of different dimensions, the monitoring and processing data of different aspects of the service functions in the early stage can be further expanded and utilized. This enables targeted service quality processing and management of different power grid service functions, improving the targeted supervision effect and differentiated management effect of the local service quality of different service functions of the service robot.

[0027] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0028] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0029] Furthermore, the functional modules in the various embodiments of the present invention 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. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A service quality monitoring system for an intelligent service robot for power grid customers, characterized in that, include: The service function quality monitoring and processing module is used to monitor and process the function quality of different service functions of the smart service robot for power grid customers, and obtain the function supervision sequence corresponding to different service functions. The service function quality multidimensional analysis module is used to perform horizontal and vertical data analysis based on the function supervision sequence corresponding to different service functions, and to use the analysis results of different dimensions to perform overall analysis and dynamic marking of the corresponding local service status of different service functions to obtain the first service function, the second service function, or the third service function. The Service Function Quality Extension and Integration Module is used to perform reliable multi-dimensional processing and analysis of the self-service content corresponding to all marked service functions, and to proactively implement targeted function content management prompts for different service functions based on the analysis results of different dimensions.

2. The service quality monitoring system for a smart service robot for power grid customers according to claim 1, characterized in that, Based on the preset regulatory cycle, multi-dimensional data monitoring and statistics are carried out on the different service functions of the smart service robot for power grid customers to obtain the total number of service failures and the total number of service transfers corresponding to different service functions. In addition, based on the service evaluations corresponding to the service failures for different service functions, the total number of satisfied and dissatisfied service failures for different service functions will be calculated based on the service evaluations. The functional reliability value corresponding to the service function is calculated based on the total number of satisfied calls without service transfer and the total number of calls without service transfer. The functional coordination value corresponding to the service function is calculated based on the total number of dissatisfied calls without service transfer, the total number of calls without service transfer, and the total number of calls without service transfer. The obtained functional reliability values ​​and functional coordination values ​​are sorted and combined to obtain the functional supervision sequence corresponding to the service function.

3. The service quality monitoring system for a smart service robot for power grid customers according to claim 2, characterized in that, The system sequentially obtains the functional reliability value and functional coordination value in the functional supervision sequence corresponding to different service functions. When performing horizontal data analysis on the functional reliability value corresponding to the service function, the functional reliability value is compared with the preset functional reliability standard value for judgment. If the functional reliability value is less than the functional reliability standard value, a functional unreliable label is generated, and the corresponding functional reliability flag is set to 1. Conversely, a functional reliability label is generated, and the corresponding functional reliability flag is set to 0.

4. The service quality monitoring system for a smart service robot for power grid customers according to claim 3, characterized in that, When performing vertical data analysis on the functional coordination values ​​corresponding to service functions, the functional coordination values ​​are compared with the preset functional coordination standard values ​​for judgment. If the function coordination value is greater than the corresponding function coordination standard value, a "function not rich" label will be generated, and the corresponding "function rich" identifier will be set to 1. Conversely, if the feature-rich tag is not specified, a feature-rich tag will be generated, and the corresponding feature-rich identifier will be set to 0.

5. The service quality monitoring system for a smart service robot for power grid customers according to claim 4, characterized in that, The functional reliability indicator and the functional richness indicator are summed, and the result is set as the service supervision value corresponding to the service function. Data analysis is performed on service monitoring values ​​to determine the local service status corresponding to service functions.

6. The service quality monitoring system for a smart service robot for power grid customers according to claim 5, characterized in that, If the service supervision value is 0, the service function to which it belongs will be marked as the first service function, and a prompt will be given indicating that the first service function is in normal local service status. If the service supervision value is 1, the service function will be marked as the second service function, and a prompt will be given indicating a minor abnormality in the local service status of the second service function. If the service supervision value is 2, the service function will be marked as a third service function, and a prompt will be given indicating a severe local service anomaly for the third service function.

7. The service quality monitoring system for a smart service robot for power grid customers according to claim 6, characterized in that, The system iterates through and analyzes all marked first service functions. If the total number of first service functions is not 0, it prompts that the self-service of the smart service robot for power grid customers is partially reliable, and prompts that the function content of all second service functions is partially managed, and prompts that the function content of all third service functions is comprehensively managed. If the total number of the first service functions is 0, a message will be displayed indicating that the self-service of the power grid customer intelligent service robot is unreliable, and a message will be displayed indicating that the overall management of the function content of all service functions will be implemented.

8. The service quality monitoring system for a smart service robot for power grid customers according to claim 6, characterized in that, Calculate and obtain the management impact value of all third-party service functions; If the management impact value is less than or equal to 0, a prompt will be made indicating that the self-service of the smart service robot for power grid customers is partially reliable, and a prompt will be made indicating that the function content of all second service functions is partially managed, and the function content of all third service functions is managed as a whole. Conversely, it will prompt that the self-service of the smart service robot for power grid customers is unreliable and provide a prompt for overall management of the function content of all service functions.

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