Data analysis method and system for information customer service

Abstract

The invention provides a data analysis method and system for information customer service, and belongs to the technical field of data processing, and the system specifically comprises a data obtaining module and a customer service distribution module, the data obtaining module is responsible for obtaining the power supply equipment of a current customer, and the customer service distribution module is responsible for distributing the power supply equipment of the current customer on the basis of the power supply equipment of the current customer. And customer service allocation processing of the current customer is carried out, so that personalized allocation processing of the customer service is realized.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular, relates to a data analysis method and system for information customer service. Background Art

[0002] With the advancement of the construction of information customer service smart customer service system, the application scenarios of information customer service intelligence have gradually expanded. Due to the large number of customer service issues and their relatively scattered distribution, how to realize data analysis and processing of customer service issues and improve the efficiency of handling customer service issues has become a technical problem that needs to be solved urgently.

[0003] In response to the above technical problems, this application aims to improve the efficiency of information system user problem handling and the intelligent service capabilities of information customer service, to achieve classification, analysis and processing of customer service issues, and to improve the work efficiency of information customer service. Specifically, this application provides a data analysis method and system for information customer service. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a data analysis system for information customer service, specifically comprising: Data acquisition module, customer service allocation module; The data acquisition module is responsible for acquiring the current customer's power supply equipment; The customer service allocation module is responsible for performing customer service allocation processing for the current customer based on the current customer's power supply equipment.

[0005] A further technical solution is that the customer's power supply equipment includes a transformer, a transmission line, a CT, and a switch device.

[0006] A further technical solution is to perform customer service allocation processing for the current customer, specifically including: Based on the power supply equipment of the current customer, the inferred problem type on the current date is determined, and the inferred problem type is used to perform customer service allocation processing for the current customer.

[0007] A further technical solution is that the inferred problem type is determined based on problem types of historical customers with power supply equipment similar to that of the current customer.

[0008] A further technical solution is that the similar historical users are historical customers of power supply equipment with a similar number that meet the requirements.

[0009] The beneficial effects of the present invention are: By acquiring the current customer's power supply equipment, we can accurately evaluate the current customer's actual operating status from the perspective of the power supply equipment. At the same time, we can also screen the possible problem types of the current customer, and lay a data foundation for differentiated customer service allocation and processing.

[0010] Based on the current customer's power supply equipment, the current customer's customer service allocation and processing are carried out, avoiding the original random customer service allocation and processing, which leads to the emergence of technical problems where the problem solving efficiency and accuracy cannot meet the requirements. It improves the personalization of customer service allocation and processing, and also improves the customer's satisfaction with problem handling.

[0011] On the other hand, the present application provides a data analysis method for information customer service, which is applied to the above-mentioned data analysis system for information customer service, specifically comprising: S1 uses weather data of different dates to divide different dates into different similar date groups, and determines common problems in the similar date groups based on analysis results of customer service problem data of different dates in different similar date groups and in combination with power supply equipment of customers corresponding to different customer service problem data; S2 determines the processing results of customer service for different types of common problems, and uses the processing results to determine matching customer service for different similar date groups; S3 determines the similar date group corresponding to the current date based on the analysis result of the weather data of the current date, and uses it as the matching date group. When it is determined that access control processing needs to be performed on the matching customer service based on the distribution data of common problems on different dates in the matching date group and the matching customer service data of the matching date group, proceeds to the next step; S4 determines similar situations of customers corresponding to different common problems based on the current customer's power supply equipment, and determines whether it is necessary to access a matching customer service based on the similar situations.

[0012] A further technical solution is that the weather data includes weather temperature, humidity and wind speed.

[0013] A further technical solution is to divide different dates into different similar date groups, specifically including: Based on the weather data between different dates, the deviation coefficients of the weather data in different dimensions on different dates are determined; Determine the deviation coefficient of weather data between different dates according to the average value of the deviation coefficient of weather data in different dimensions; Different dates are divided into different similar date groups based on the weather data deviation coefficient.

[0014] A further technical solution is that the deviation coefficient of the weather data is determined according to the ratio of the deviation amount of the weather data in the dimension on different dates to a preset deviation amount.

[0015] A further technical solution is to determine whether it is necessary to access the matching customer service, including: Determine the similarity number of power supply topologies of customers corresponding to different common problems based on the similarity of the current power supply topologies of customers, and determine the similarity coefficient of power supply topologies of customers corresponding to different common problems using the similarity number; Based on the similarity of power supply equipment in the current power supply topology of the customer, determine the same number of power supply equipment of the customer corresponding to different common problems, and use the same number to determine the similarity coefficient of the power supply equipment of the customer corresponding to different common problems; According to the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficients of customers corresponding to different common problems are determined, and the customer data similarity coefficients of customers corresponding to different common problems are used to determine whether it is necessary to access the matching customer service.

[0016] A further technical solution is to use the customer data similarity coefficient of customers corresponding to different common problems to determine whether it is necessary to connect to the matching customer service, specifically including: By using the customer data similarity coefficients of customers corresponding to different common problems, data-similar customers among the customers are determined, and according to the number of the data-similar customers, it is determined whether it is necessary to connect to matching customer service.

[0017] A further technical solution is that when the number of data-similar customers is greater than a preset number of similar customers, it is determined that access to a matching customer service is required.

[0018] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0021] Figure 1 It is a framework diagram of a data analysis system for information customer service; Figure 2is a flow chart of a data analysis method for information customer service; Figure 3 is a flow chart of a method for determining common problems in groups of similar dates; Figure 4 It is a flow chart of a method for determining matching customer services for similar date groups. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0023] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0024] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a data analysis system for information customer service is provided, specifically comprising: Data acquisition module, customer service allocation module; The data acquisition module is responsible for acquiring the current customer's power supply equipment; The customer service allocation module is responsible for performing customer service allocation processing for the current customer based on the current customer's power supply equipment.

[0025] Furthermore, the customer's power supply equipment includes transformers, transmission lines, CTs, and switchgear.

[0026] Specifically, the customer service allocation process for the current customer is carried out, including: Based on the power supply equipment of the current customer, the inferred problem type on the current date is determined, and the inferred problem type is used to perform customer service allocation processing for the current customer.

[0027] It should be noted that the inferred problem type is determined based on the problem types of historical customers whose power supply equipment is similar to that of the current customer.

[0028] It can be understood that the similar historical users are historical customers of the power supply equipment with a similar number that meet the requirements.

[0029] Example 2 On the other hand, Figure 2 As shown, the present application provides a data analysis method for information customer service, which is applied to the above-mentioned data analysis system for information customer service, specifically including: S1 uses weather data of different dates to divide different dates into different similar date groups, and determines common problems in the similar date groups based on analysis results of customer service problem data of different dates in different similar date groups and in combination with power supply equipment of customers corresponding to different customer service problem data; Furthermore, the weather data includes weather temperature, humidity and wind speed.

[0030] Specifically, different dates are divided into different similar date groups, including: Based on the weather data between different dates, the deviation coefficients of the weather data in different dimensions on different dates are determined; Determine the deviation coefficient of weather data between different dates according to the average value of the deviation coefficient of weather data in different dimensions; Different dates are divided into different similar date groups based on the weather data deviation coefficient.

[0031] It should be noted that the deviation coefficient of the weather data is determined according to the ratio of the deviation amount of the weather data in the dimension on different dates to a preset deviation amount.

[0032] Furthermore, different dates are divided into different similar date groups based on the weather data deviation coefficient, specifically including: The dates whose weather data deviation coefficient is within the preset range are divided into the same similar date group.

[0033] It is understandable that the customer service problem data includes customer service problems and customers corresponding to the customer service problems.

[0034] Specifically, the customer service issues include power outages, voltage fluctuations, meter failures, frequent tripping, and low voltage.

[0035] Specifically, Figure 3 As shown, the method for determining the common problems in the similar date groups is: Based on the analysis results of the customer service problem data on different dates in the similar date group, customers with customer service problems on different dates are determined and used as matching problem customers; Determine the power supply equipment similarity coefficient between different matching problem users according to the proportion of the same number of power supply equipment of different matching problem customers, and determine similar problem users among the matching problem users using the power supply equipment similarity coefficient; Determine whether the customer service problem is a common problem based on distribution data of customers with similar problems on different dates.

[0036] Furthermore, when the proportion of the number of dates in which customers with similar problems exist in the similar date group is greater than the proportion of the preset number of dates and the number of users with similar problems is greater than the preset number of users with similar problems, it is determined that the customer service problems corresponding to the users with similar problems are common problems.

[0037] S2 determines the processing results of customer service for different types of common problems, and uses the processing results to determine matching customer service for different similar date groups; It can be understood that the processing results of the common problems include accurate processing and error processing.

[0038] Specifically, Figure 4 As shown, the method for determining the matching customer service of the similar date group is: Based on the common problems of the similar date groups, determining the number ratio of dates with common problems in the similar date groups, and using the number ratio of dates with the common problems, determining weight coefficients of different types of common problems; Determine the matching coefficient between the customer service and different types of common problems based on the proportion of different types of common problems accurately handled by the customer service; The problem matching coefficient of the customer service is determined by the sum of the products of the weight coefficients of different types of common problems and the matching coefficients, and the problem matching coefficient is used to determine whether the customer service is a matching customer service.

[0039] Furthermore, when the question matching coefficient is greater than a preset matching coefficient, the customer service is determined to be a matching customer service.

[0040] Optionally, the method for determining the matching customer service of the similar date group is: Determine the matching coefficient between the customer service and different types of common problems based on the proportion of different types of common problems accurately handled by the customer service; Whether the customer service is a matching customer service is determined by using an average value of the matching coefficients of the customer service for different types of common questions in the similar date groups.

[0041] Optionally, the method for determining the matching customer service of the similar date group is: S21 determines the matching coefficient between the customer service and different types of common problems based on the proportion of different types of common problems accurately handled by the customer service and the number of common problems of the type handled by the customer service; S22: based on the common problems of the similar date groups, determining the percentage of dates with common problems in the similar date groups, and determining weight coefficients of different types of common problems using the percentage of dates with common problems and the number of common problems on different dates; S23 determines the problem matching coefficient of the customer service by summing the product of the weight coefficients of different types of common problems and the matching coefficients, and uses the problem matching coefficient to determine whether the customer service is a matching customer service.

[0042] Optionally, the above step S21 includes the following contents: S211: Based on the percentage of different types of common problems accurately handled by the customer service, if it is determined that the percentage of different types of common problems accurately handled by the customer service does not meet the requirements, then it is determined that the customer service does not belong to the matching customer service. When there are common problems whose percentage of accurate handling meets the requirements, the process proceeds to step S212; S212 regards the common problems whose percentage of accurately processed problems meets the requirement as accurately processed problems. When the total number of accurately processed problems processed by the customer service meets the requirement, the process proceeds to step S213. When the total number of accurately processed problems processed by the customer service does not meet the requirement, it is determined that the customer service does not belong to the matching customer service. S213 determines the matching coefficient between the customer service and different types of common problems based on the proportion of the number of different types of common problems accurately handled by the customer service and the number of common problems of the type handled by the customer service. When there is a common problem whose matching coefficient meets the requirement, the process proceeds to step S214. When there is no common problem whose matching coefficient meets the requirement, it is determined that the customer service does not belong to the matching customer service. S214: When the average value of the matching coefficients between the customer service and different types of common problems does not meet the requirements, it is determined that the customer service does not belong to the matching customer service. When the average value of the matching coefficients between the customer service and different types of common problems meets the requirements, proceed to step S22.

[0043] Optionally, the above step S22 includes the following contents: S221 determines the percentage of dates with common problems in the similar date groups based on the common problems of the similar date groups, and determines weight coefficients of different types of common problems using the percentage of dates with common problems and the number of common problems in different dates; S222: Common problems with weight coefficients greater than the preset weight coefficients are regarded as important common problems. When the average value of the matching coefficients between the customer service and different types of important common problems does not meet the requirements, it is determined that the customer service does not belong to the matching customer service. When the average value of the matching coefficients between the customer service and different types of important common problems meets the requirements, the process proceeds to step S223. S223 determines the corrected matching coefficients of different important issues by multiplying the weight coefficients of different important issues by the matching coefficients. When there are important issues whose corrected matching coefficients do not meet the requirements, it is determined that the customer service does not belong to the matched customer service. When there are no important issues whose corrected matching coefficients do not meet the requirements, the process proceeds to step S23.

[0044] S3 determines the similar date group corresponding to the current date based on the analysis result of the weather data of the current date, and uses it as the matching date group. When it is determined that access control processing needs to be performed on the matching customer service based on the distribution data of common problems on different dates in the matching date group and the matching customer service data of the matching date group, proceeds to the next step; Further, it is determined that access control processing needs to be performed on the matching customer service, specifically including: Determine the average number of common problems on different dates based on the distribution data of common problems on different dates in the matching date group; Determining the number of matching customer services in the matching date group based on the matching customer service data in the matching date group; The processing quantity ratio of common problems is determined according to the ratio of the average number of the common problems to the number of matched customer services, and the processing quantity ratio is used to determine whether access control processing needs to be performed on the matched customer services.

[0045] It should also be noted that when the processing quantity ratio is greater than a preset ratio threshold, it is determined that access control processing needs to be performed on the matching customer service.

[0046] Optionally, determining that access control processing needs to be performed on the matching customer service specifically includes: Determine the number of matching customer services in the matching date group based on the matching customer service data in the matching date group, and when the number of matching customer services in the matching date group is greater than a preset number of customer services, determine that access control processing does not need to be performed on the matching customer services; When the number of matching customer services in the matching date group is not greater than the preset number of customer services: Determine the average number of common problems on different dates based on the distribution data of common problems on different dates in the matching date group, and when the average number of common problems on different dates is greater than a preset number of problems, determine that access control processing needs to be performed on the matching customer service; When the average number of common questions on different dates is not greater than the preset number of questions: Obtaining a percentage of dates on which the number of common problems is greater than a preset number of common problems, and when the percentage of dates on which the number of common problems is greater than the preset number of common problems does not meet the requirement, determining that access control processing needs to be performed on the matching customer service; When the number of common problems is greater than the preset number of common problems and the proportion of the number of dates meets the requirements: The number of different types of common problems on different dates is used to determine the difficulty of handling common problems on different dates. When the number of dates with a difficulty of handling common problems greater than a preset difficulty does not meet the requirement, it is determined that the matching customer service needs to be placed on hold; When the difficulty of handling common problems is greater than the number of dates with preset difficulty, the requirements are met: The difficulty processing ratio of common problems is determined based on the ratio of the average processing difficulty of common problems on different dates to the number of matching customer services, and the difficulty processing ratio is used to determine whether access control processing is required for the matching customer service.

[0047] Further, when the difficulty processing ratio is greater than a preset difficulty ratio threshold, it is determined that access control processing needs to be performed on the matched customer service.

[0048] S4 determines similar situations of customers corresponding to different common problems based on the current customer's power supply equipment, and determines whether it is necessary to access a matching customer service based on the similar situations.

[0049] Specifically, the similar situations include similar numbers of power supply topologies and the same number of power supply devices in the power supply topologies.

[0050] It is understandable that determining whether to access the matching customer service specifically includes: Determine the similarity number of power supply topologies of customers corresponding to different common problems based on the similarity of the current power supply topologies of customers, and determine the similarity coefficient of power supply topologies of customers corresponding to different common problems using the similarity number; Based on the similarity of power supply equipment in the current power supply topology of the customer, determine the same number of power supply equipment of the customer corresponding to different common problems, and use the same number to determine the similarity coefficient of the power supply equipment of the customer corresponding to different common problems; According to the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficients of customers corresponding to different common problems are determined, and the customer data similarity coefficients of customers corresponding to different common problems are used to determine whether it is necessary to access the matching customer service.

[0051] Furthermore, the customer data similarity coefficients of customers corresponding to different common problems are used to determine whether it is necessary to connect to matching customer service, specifically including: By using the customer data similarity coefficients of customers corresponding to different common problems, data-similar customers among the customers are determined, and according to the number of the data-similar customers, it is determined whether it is necessary to connect to matching customer service.

[0052] Specifically, the data-similar customers are customers corresponding to common problems whose customer data similarity coefficient is greater than a preset similarity coefficient.

[0053] It should be noted that when the number of data-similar customers is greater than the preset number of similar customers, it is determined that access to matching customer service is required.

[0054] In another possible embodiment, determining whether to access the matching customer service specifically includes: Based on the similarity of the current customer's power supply topology, determine the similarity number of customers' power supply topologies corresponding to different common problems, and use the similarity number to determine the power supply topology similarity coefficient of customers corresponding to different common problems. When there is no customer corresponding to the common problem whose power supply topology similarity coefficient is greater than the preset topology similarity coefficient, determine that it is not necessary to connect to the matching customer service; When there are customers with common problems whose power supply topology similarity coefficient is greater than the preset topology similarity coefficient: The customers corresponding to the common problems whose power supply topology similarity coefficient is greater than the preset topology similarity coefficient are regarded as topology similar customers. When the number of the topology similar customers is less than the preset number of topology customers, it is determined that there is no need to connect to the matching customer service; When the number of topology-similar customers is not less than the preset number of topology customers: Based on the similarity of the power supply equipment in the current customer's power supply topology, determine the same number of power supply equipment as customers with similar different topologies. When the same number of power supply equipment as customers with similar different topologies does not meet the requirements, it is determined that there is no need to access the matching customer service. When there are customers with similar topology and the same number of PSEs that meet the requirements: Determine the power supply equipment similarity coefficient with different topology-similar customers by using the same number, and when the number of topology-similar customers whose power supply equipment similarity coefficients meet the requirements does not meet the requirements, determine that there is no need to access the matching customer service; When the number of topologically similar customers whose power supply equipment similarity coefficients meet the requirements meets the requirements: According to the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficient with different topology similar customers is determined, and the customer data similarity coefficient with different topology similar customers is used to determine whether it is necessary to access the matching customer service.

[0055] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0056] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A data analysis system for information customer service, characterized in that: Specifically include: Data acquisition module, customer service allocation module; The data acquisition module is responsible for acquiring the current customer's power supply equipment; The customer service allocation module is responsible for performing customer service allocation processing for the current customer based on the current customer's power supply equipment.

2. The data analysis system for information customer service according to claim 1, characterized in that: The customer's power supply equipment includes transformers, transmission lines, CTs, and switchgear.

3. The data analysis system for information customer service according to claim 1, characterized in that: Carry out customer service allocation processing for current customers, including: Based on the power supply equipment of the current customer, the inferred problem type on the current date is determined, and the inferred problem type is used to perform customer service allocation processing for the current customer.

4. The data analysis system for information customer service as claimed in claim 3, characterized in that: The inferred problem type is determined based on problem types of historical customers similar to the power supply equipment of the current customer.

5. A data analysis method for information customer service, characterized in that: A data analysis system for information customer service as described in any one of claims 1-2, characterized in that it specifically includes: Using weather data of different dates to divide different dates into different similar date groups, according to the analysis results of customer service problem data of different dates in different similar date groups, and in combination with the power supply equipment of customers corresponding to different customer service problem data, the common problems in the similar date groups are determined; Determine the processing results of customer service for different types of common problems, and use the processing results to determine matching customer service for different similar date groups; Determine the similar date group corresponding to the current date based on the analysis result of the weather data of the current date, and use it as the matching date group. When it is determined that access control processing needs to be performed on the matching customer service based on the distribution data of common problems on different dates in the matching date group and the matching customer service data of the matching date group, proceed to the next step; Based on the current customer's power supply equipment, similar situations of customers corresponding to different common problems are determined, and based on the similar situations, it is determined whether it is necessary to access the matching customer service.

6. The data analysis method for information customer service according to claim 5, characterized in that: The weather data includes weather temperature, humidity and wind speed.

7. The data analysis method for information customer service according to claim 5, characterized in that: Group different dates into different similar date groups, including: Based on the weather data between different dates, the deviation coefficients of the weather data in different dimensions on different dates are determined; Determine the deviation coefficient of weather data between different dates according to the average value of the deviation coefficient of weather data in different dimensions; Different dates are divided into different similar date groups based on the weather data deviation coefficient.

8. The data analysis method for information customer service according to claim 5, characterized in that: Different dates are divided into different similar date groups based on the weather data deviation coefficient, specifically including: The dates whose weather data deviation coefficient is within the preset range are divided into the same similar date group.

9. The data analysis method for information customer service according to claim 5, characterized in that: Determine whether you need to access the matching customer service, including: Determine the similarity number of power supply topologies of customers corresponding to different common problems based on the similarity of the current power supply topologies of customers, and determine the similarity coefficient of power supply topologies of customers corresponding to different common problems using the similarity number; Based on the similarity of power supply equipment in the current power supply topology of the customer, determine the same number of power supply equipment of the customer corresponding to different common problems, and use the same number to determine the similarity coefficient of the power supply equipment of the customer corresponding to different common problems; According to the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficients of customers corresponding to different common problems are determined, and the customer data similarity coefficients of customers corresponding to different common problems are used to determine whether it is necessary to access the matching customer service.

10. The data analysis method for information customer service according to claim 9, characterized in that: Use the customer data similarity coefficients of customers corresponding to different common problems to determine whether they need to be connected to matching customer service, including: By using the customer data similarity coefficients of customers corresponding to different common problems, data-similar customers among the customers are determined, and according to the number of the data-similar customers, it is determined whether it is necessary to connect to matching customer service.

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