A data analysis method and system for information customer service

Abstract

This invention provides a data analysis method and system for information customer service, belonging to the field of data processing technology. Specifically, it includes: a data acquisition module and a customer service allocation module. The data acquisition module is responsible for acquiring the power supply equipment of the current customer, and the customer service allocation module is responsible for allocating customer service based on the power supply equipment of the current customer, thereby realizing personalized allocation of customer service.

Description

Technical Field

[0001] This 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 Technology

[0002] With the advancement of the construction of the intelligent customer service system, the application scenarios of intelligent customer service are gradually expanding. Due to the large number of customer service issues and their relatively scattered distribution, how to achieve data analysis and processing of customer service issues and improve the efficiency of customer service issue processing has become an urgent technical problem to be solved.

[0003] To address the aforementioned technical issues, this application aims to improve the efficiency of handling user issues in information systems and enhance the intelligent service capabilities of information customer service. Specifically, it provides a data analysis method and system for information customer service, enabling the categorization, analysis, and processing of customer service issues to improve the efficiency of information customer service work. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted:

[0005] Firstly, this application provides a data analysis system for information customer service, specifically including:

[0006] Data acquisition module, customer service assignment module;

[0007] The data acquisition module is responsible for acquiring the power supply equipment of the current customer.

[0008] The customer service allocation module is responsible for allocating customer service for the current customer based on the current customer's power supply equipment.

[0009] A further technical solution is that the customer's power supply equipment includes transformers, transmission lines, CTs, and switchgear.

[0010] A further technical solution involves assigning customer service representatives to the current client, specifically including:

[0011] Based on the current customer's power supply equipment, determine the predicted problem type for the current date, and use the predicted problem type to perform customer service assignment processing for the current customer.

[0012] A further technical solution is that the inferred problem type is determined based on the problem types of historical customers similar to the current customer's power supply equipment.

[0013] A further technical solution is that the similar historical users are historical customers whose power supply equipment has a similar quantity that meets the requirements.

[0014] The beneficial effects of this invention are as follows:

[0015] By acquiring information about the current customers' power supply equipment, we were able to accurately assess the actual operating status of the current customers from the perspective of the power supply equipment. At the same time, we were able to screen the possible types of problems of the current customers, and also laid the data foundation for differentiated customer service allocation and processing.

[0016] Based on the current customer's power supply equipment, customer service is assigned to the current customer, avoiding the technical problems that caused the original random assignment of customer service, which resulted in the inefficiency and accuracy of problem resolution. This improves the personalization of customer service assignment and also enhances customer satisfaction with problem handling.

[0017] On the other hand, this application provides a data analysis method for information customer service, applied to the aforementioned data analysis system for information customer service, specifically including:

[0018] S1 uses weather data from different dates to divide different dates into different similar date groups. Based on the analysis results of customer service problem data for different dates in different similar date groups, and combined with the power supply equipment of customers corresponding to different customer service problem data, the common problems in the similar date groups are determined.

[0019] S2 determines the customer service representative's handling results for common issues of different types, and uses the handling results to determine the matching customer service representatives for different similar date groups;

[0020] S3 uses the analysis results of the weather data for the current date to determine the similar date group corresponding to the current date, and uses it as the matching date group. Based on the distribution data of common problems of different dates in the matching date group and the matching customer service data of the matching date group, when it is determined that access control processing needs to be performed on the matching customer service, proceed to the next step.

[0021] S4, based on the current customer's power supply equipment, identifies similar situations of customers corresponding to different common problems, and determines whether it is necessary to connect to a matching customer service representative based on the similar situations.

[0022] A further technical solution is that the weather data includes weather temperature, humidity, and wind speed.

[0023] A further technical solution involves dividing different dates into different similar date groups, specifically including:

[0024] Based on weather data from different dates, determine the deviation coefficients of weather data from different dates in different dimensions;

[0025] Determine the weather data deviation coefficient between different dates based on the average value of the deviation coefficients of weather data from different dimensions;

[0026] Based on the weather data deviation coefficient, different dates are divided into different similar date groups.

[0027] A further technical solution is that the deviation coefficient of the weather data is determined based on the ratio of the deviation of the weather data in the dimension on different dates to a preset deviation.

[0028] A further technical solution involves determining whether it is necessary to connect to a matched customer service representative, specifically including:

[0029] Based on the similarity of the current customer power supply topology, determine the number of similarities in the power supply topology of customers corresponding to different common problems, and use the number of similarities to determine the power supply topology similarity coefficient of customers corresponding to different common problems;

[0030] Based on the similarity of power supply equipment in the current customer power supply topology, determine the same number of power supply equipment for customers corresponding to different common problems, and use the same number to determine the similarity coefficient of power supply equipment for customers corresponding to different common problems;

[0031] Based on the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficient corresponding to different common problems is determined, and the customer data similarity coefficient corresponding to different common problems is used to determine whether it is necessary to connect to the matching customer service.

[0032] A further technical solution involves using the similarity coefficient of customer data corresponding to different common problems to determine whether it is necessary to connect to a matching customer service representative. Specifically, this includes:

[0033] By using the customer data similarity coefficients of customers corresponding to different common problems, data-similar customers are identified among the customers, and the number of data-similar customers determines whether it is necessary to connect them to a matching customer service representative.

[0034] A further technical solution is that when the number of customers with similar data is greater than the preset number of similar customers, it is determined that it is necessary to connect to the matching customer service.

[0035] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0037] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0038] Figure 1 This is a framework diagram of a data analysis system for information customer service.

[0039] Figure 2 This is a flowchart of a data analysis method used for information customer service.

[0040] Figure 3 This is a flowchart illustrating the method for determining commonalities in groups of similar dates;

[0041] Figure 4 This is a flowchart illustrating the method for determining customer service representatives who match groups of similar dates. Detailed Implementation

[0042] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0043] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that there may be other elements / components / etc. in addition to the listed elements / components / etc.

[0044] Example 1

[0045] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, according to one aspect of the present invention, a data analysis system for information customer service is provided, specifically comprising:

[0046] Data acquisition module, customer service assignment module;

[0047] The data acquisition module is responsible for acquiring the power supply equipment of the current customer.

[0048] The customer service allocation module is responsible for allocating customer service for the current customer based on the current customer's power supply equipment.

[0049] Furthermore, the customer's power supply equipment includes transformers, transmission lines, CTs, and switchgear.

[0050] Specifically, the current customer service assignment process includes:

[0051] Based on the current customer's power supply equipment, determine the predicted problem type for the current date, and use the predicted problem type to perform customer service assignment processing for the current customer.

[0052] It should be noted that the inferred problem type is determined based on the problem types of historical customers with similar power supply equipment to the current customer.

[0053] It is understood that the similar historical users are historical customers whose power supply equipment has a similar number that meets the requirements.

[0054] Example 2

[0055] On the other hand, such as Figure 2 As shown, this application provides a data analysis method for information customer service, applied to the aforementioned data analysis system for information customer service, specifically including:

[0056] S1 uses weather data from different dates to divide different dates into different similar date groups. Based on the analysis results of customer service problem data for different dates in different similar date groups, and combined with the power supply equipment of customers corresponding to different customer service problem data, the common problems in the similar date groups are determined.

[0057] Furthermore, the weather data includes temperature, humidity, and wind speed.

[0058] Specifically, different dates are divided into different similar date groups, including:

[0059] Based on weather data from different dates, determine the deviation coefficients of weather data from different dates in different dimensions;

[0060] Determine the weather data deviation coefficient between different dates based on the average value of the deviation coefficients of weather data from different dimensions;

[0061] Based on the weather data deviation coefficient, different dates are divided into different similar date groups.

[0062] It should be noted that the deviation coefficient of the weather data is determined based on the ratio of the deviation of the weather data in this dimension on different dates to a preset deviation.

[0063] Furthermore, based on the weather data deviation coefficient, different dates are divided into different similar date groups, specifically including:

[0064] Dates with weather data deviation coefficients within a preset range are grouped into the same similar date group.

[0065] It is understood that the customer service issue data includes customer service issues and the customers corresponding to those issues.

[0066] Specifically, the customer service issues include power outages, voltage fluctuations, meter malfunctions, frequent tripping, and low voltage.

[0067] Specifically, such as Figure 3 As shown, the method for determining the commonalities in the similar date groups is as follows:

[0068] Based on the analysis results of customer service issue data for different dates in the similar date group, customers with customer service issues on different dates are identified and used as matched issue customers;

[0069] Based on the proportion of identical power supply equipment among different customers with matching problems, determine the power supply equipment similarity coefficient between different users with matching problems, and use the power supply equipment similarity coefficient to determine similar users among the users with matching problems;

[0070] The distribution data of customers with similar problems on different dates is used to determine whether the customer service problem is a common problem.

[0071] Furthermore, when the percentage of dates with customers having similar problems in the similar date group is greater than the percentage of dates with similar problems and the number of users with similar problems is greater than the number of users with similar problems, then the customer service problem corresponding to the user with similar problems is determined to be a common problem.

[0072] S2 determines the customer service representative's handling results for common issues of different types, and uses the handling results to determine the matching customer service representatives for different similar date groups;

[0073] It is understood that the processing results for the common problems include accurate processing and error processing.

[0074] Specifically, such as Figure 4 As shown, the method for determining the matching customer service representatives for the similar date group is as follows:

[0075] Based on the common problems of the similar date groups, determine the proportion of dates with common problems in the similar date groups, and use the proportion of dates with common problems to determine the weight coefficients of different types of common problems;

[0076] Based on the proportion of common issues of different types accurately handled by the customer service representatives, the matching coefficient between the customer service representatives and common issues of different types is determined.

[0077] The customer service representative's question matching coefficient is determined by summing the products of the weight coefficients and matching coefficients for different types of common questions, and then the customer service representative is used to determine whether the customer service representative is a matching customer service representative.

[0078] Furthermore, when the matching coefficient of the question is greater than the preset matching coefficient, the customer service representative is determined to be a matched customer service representative.

[0079] Optionally, the method for determining the matching customer service representatives for the aforementioned similar date groups is as follows:

[0080] Based on the proportion of common issues of different types accurately handled by the customer service representatives, the matching coefficient between the customer service representatives and common issues of different types is determined.

[0081] The customer service representative is determined to be a matching customer service representative by using the average of the matching coefficients of the customer service representatives for common questions of different types in the similar date group.

[0082] Optionally, the method for determining the matching customer service representatives for the similar date groups is as follows:

[0083] S21 determines the matching coefficient between the customer service representative and the different types of common questions based on the proportion of the number of times the customer service representative accurately handles common questions of different types, and in combination with the number of times the customer service representative handles common questions of the same type.

[0084] S22 Based on the common problems of the similar date groups, determine the proportion of dates with common problems in the similar date groups, and use the proportion of dates with common problems and the number of common problems in different dates to determine the weight coefficients of different types of common problems;

[0085] S23 determines the customer service representative's question matching coefficient by summing the products of the weight coefficients and matching coefficients of different types of common questions, and uses the question matching coefficient to determine whether the customer service representative is a matching customer service representative.

[0086] Optionally, step S21 above includes the following:

[0087] S211 Based on the percentage of common problems of different types accurately handled by the customer service representative, if the percentage of accurate handling of common problems of different types by the customer service representative does not meet the requirements, then the customer service representative is determined not to be a matched customer service representative. When there are common problems whose percentage of accurate handling meets the requirements, proceed to step S212.

[0088] S212 identifies common issues whose accuracy percentage meets the requirements as accurate processing issues. When the accuracy of these issues meets the requirements for the total number of times they are processed by customer service representatives, the process proceeds to step S213. If the accuracy of these issues does not meet the requirements for the total number of times they are processed by customer service representatives, then it is determined that the customer service representative is not a matched customer service representative.

[0089] S213 determines the matching coefficient between the customer service representative and the common problems of different types based on the percentage of accurate handling of common problems of different types by the customer service representative, and in combination with the number of common problems of the same type handled by the customer service representative. If there are common problems with matching coefficients that meet the requirements, then proceed to step S214. If there are no common problems with matching coefficients that meet the requirements, then it is determined that the customer service representative is not a matched customer service representative.

[0090] S214 If the average value of the matching coefficients between the customer service representative and the common problems of different types does not meet the requirements, then it is determined that the customer service representative does not belong to the matching customer service representatives. If the average value of the matching coefficients between the customer service representative and the common problems of different types meets the requirements, proceed to step S22.

[0091] Optionally, step S22 above includes the following:

[0092] S221 Based on the common problems of the similar date groups, determine the proportion of dates with common problems in the similar date groups, and use the proportion of dates with common problems and the number of common problems in different dates to determine the weight coefficients of different types of common problems;

[0093] S222 takes common problems with weight coefficients greater than preset weight coefficients as important common problems. When the average matching coefficient of the customer service representative with different types of important common problems does not meet the requirements, it is determined that the customer service representative is not a matching customer service representative. When the average matching coefficient of the customer service representative with different types of important common problems meets the requirements, the process proceeds to step S223.

[0094] S223 determines the corrected matching coefficient for different important issues by multiplying the weight coefficient of the important issues with the matching coefficient. If there is an important issue whose corrected matching coefficient does not meet the requirements, it is determined that the customer service representative does not belong to the matching customer service representative. If there is no important issue whose corrected matching coefficient does not meet the requirements, the process proceeds to step S23.

[0095] S3 uses the analysis results of the weather data for the current date to determine the similar date group corresponding to the current date, and uses it as the matching date group. Based on the distribution data of common problems of different dates in the matching date group and the matching customer service data of the matching date group, when it is determined that access control processing needs to be performed on the matching customer service, proceed to the next step.

[0096] Furthermore, it was determined that access control processing was required for the matched customer service representatives, specifically including:

[0097] Based on the distribution data of common issues across different dates in the matching date group, determine the average number of common issues across different dates;

[0098] Based on the matched customer service data in the matched date group, determine the number of matched customer service representatives in the matched date group;

[0099] Based on the ratio of the average number of common problems to the number of matched customer service representatives, the processing quantity ratio of common problems is determined, and the processing quantity ratio is used to determine whether access control processing is required for the matched customer service representatives.

[0100] It should also be noted that when the processing quantity ratio is greater than the preset ratio threshold, it is determined that access control processing needs to be performed on the matched customer service.

[0101] Optionally, it is determined that access control processing is required for the matched customer service representatives, specifically including:

[0102] Based on the matched customer service data in the matched date group, the number of matched customer service representatives in the matched date group is determined. When the number of matched customer service representatives in the matched date group is greater than the preset number of customer service representatives, it is determined that no access control processing is required for the matched customer service representatives.

[0103] When the number of matched customer service representatives in the matched date group is not greater than the preset number of customer service representatives:

[0104] Based on the distribution data of common questions on different dates in the matching date group, the average number of common questions on different dates is determined. When the average number of common questions on different dates is greater than the preset number of questions, it is determined that access control processing needs to be performed on the matched customer service.

[0105] When the average number of common questions across different dates is not greater than the preset number of questions:

[0106] The percentage of days on which the number of common questions exceeds the preset number of common questions is determined. If the percentage of days on which the number of common questions exceeds the preset number of common questions does not meet the requirements, it is determined that access control processing needs to be performed on the matched customer service.

[0107] When the percentage of days with a number of common problems exceeding the preset number of common problems meets the requirement:

[0108] The processing difficulty of common problems on different dates is determined by the number of common problems of different types on different dates. When the number of dates where the processing difficulty of common problems is greater than the preset processing difficulty does not meet the requirements, it is determined that the matched customer service needs to be put into idle status.

[0109] When the number of days where the difficulty of handling common problems exceeds the preset difficulty meets the requirement:

[0110] Based on the ratio of the average difficulty of handling common issues on different dates to the number of matched customer service representatives, a difficulty handling ratio for common issues is determined, and the difficulty handling ratio is used to determine whether access control processing is required for the matched customer service representatives.

[0111] Furthermore, 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 representative.

[0112] S4, based on the current customer's power supply equipment, identifies similar situations of customers corresponding to different common problems, and determines whether it is necessary to connect to a matching customer service representative based on the similar situations.

[0113] Specifically, the similarities include the number of similar power supply topologies and the number of identical power supply devices within the power supply topologies.

[0114] Understandably, determining whether to connect to a matched customer service representative involves the following:

[0115] Based on the similarity of the current customer power supply topology, determine the number of similarities in the power supply topology of customers corresponding to different common problems, and use the number of similarities to determine the power supply topology similarity coefficient of customers corresponding to different common problems;

[0116] Based on the similarity of power supply equipment in the current customer power supply topology, determine the same number of power supply equipment for customers corresponding to different common problems, and use the same number to determine the similarity coefficient of power supply equipment for customers corresponding to different common problems;

[0117] Based on the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficient corresponding to different common problems is determined, and the customer data similarity coefficient corresponding to different common problems is used to determine whether it is necessary to connect to the matching customer service.

[0118] Furthermore, by utilizing the similarity coefficient of customer data corresponding to different common problems, it is determined whether it is necessary to connect to a matching customer service representative. Specifically, this includes:

[0119] By using the customer data similarity coefficients of customers corresponding to different common problems, data-similar customers are identified among the customers, and the number of data-similar customers determines whether it is necessary to connect them to a matching customer service representative.

[0120] Specifically, the data-similar customers are those customers whose data similarity coefficient is greater than a preset similarity coefficient and who share common problems.

[0121] It should be noted that when the number of customers with similar data exceeds the preset number of similar customers, it is determined that it is necessary to connect to the matching customer service.

[0122] In another possible embodiment, determining whether it is necessary to connect to a matched customer service representative specifically includes:

[0123] Based on the similarity of the current customer power supply topology, determine the number of similarity between the power supply topology of customers corresponding to different common problems. Use the number of similarity to determine the power supply topology similarity coefficient of customers corresponding to different common problems. When there are no customers corresponding to common problems with a power supply topology similarity coefficient greater than the preset topology similarity coefficient, it is determined that there is no need to connect to the matching customer service.

[0124] When there are customers with a common problem whose power supply topology similarity coefficient is greater than the preset topology similarity coefficient:

[0125] Customers with common problems whose power supply topology similarity coefficient is greater than the preset topology similarity coefficient are identified as topology similar customers. When the number of such topology similar customers is less than the preset number of topology customers, it is determined that they do not need to be connected to the matching customer service.

[0126] When the number of customers with similar topologies is not less than the preset number of customers with topologies:

[0127] Based on the similarity of power supply equipment in the current customer's power supply topology, determine the same number of power supply equipment as customers with similar topologies in different topologies. If the same number of power supply equipment as customers with similar topologies in different topologies does not meet the requirements, it is determined that it is not necessary to connect to the matching customer service.

[0128] When there are topologically similar customers with the same number of power supply devices that meet the requirements:

[0129] The same number is used to determine the power supply equipment similarity coefficient with different topology similar customers. When the number of topology similar customers whose power supply equipment similarity coefficient meets the requirements does not meet the requirements, it is determined that no connection to the matching customer service is required.

[0130] When the number of topologically similar customers whose power supply equipment similarity coefficient meets the requirement is met:

[0131] Based on the power supply topology similarity coefficient and the power supply equipment similarity coefficient, the customer data similarity coefficient with customers similar to different topologies is determined, and the customer data similarity coefficient with customers similar to different topologies is used to determine whether it is necessary to connect to the matching customer service.

[0132] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0133] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0134] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A data analysis system for information customer service, characterized by, Specifically comprising: a data acquisition module, a customer service allocation module; The data acquisition module is responsible for acquiring the power supply equipment of the current customer; The customer service allocation module is responsible for allocating customer service to the current customer based on the current customer's power supply equipment; Different dates are divided into different similar date groups using weather data of different dates, and the common problems in the similar date groups are determined according to the analysis results of the customer service problem data of different dates in different similar date groups and the customer's power supply equipment corresponding to different customer service problem data; Determine the processing results of the customer service in different types of common problems, and use the processing results to determine the matching customer service of different similar date groups; Determine the similar date group corresponding to the current date based on the analysis results of the weather data of the current date, and use it as the matching date group. When it is determined that the matching customer service needs to be accessed for control processing, go to the next step; Determine the similar situation of the customer corresponding to different common problems based on the current customer's power supply equipment, and determine whether to access the matching customer service based on the similar situation; Determine that the matching customer service needs to be accessed for control processing, specifically including: Determine the average number of common problems in different dates based on the distribution data of common problems in different dates in the matching date group; Determine the number of matching customer service in the matching date group based on the matching customer service data in the matching date group; Determine the processing quantity ratio of common problems according to the ratio of the average number of common problems to the number of matching customer service, and use the processing quantity ratio to determine whether the matching customer service needs to be accessed for control processing; When the processing quantity ratio is greater than a preset ratio threshold, it is determined that the matching customer service needs to be accessed for control processing; Determine whether to access the matching customer service, specifically including: Determine the similar number of power supply topologies of customers corresponding to different common problems based on the similar situation of the current customer's power supply topology, and use the similar number to determine the power supply topology similarity coefficient of customers corresponding to different common problems; Determine the same number of power supply equipment of customers corresponding to different common problems based on the similar situation of the power supply equipment in the current customer's power supply topology, and use the same number to determine the power supply equipment similarity coefficient of customers corresponding to different common problems; Determine the customer data similarity coefficient of customers corresponding to different common problems according to the power supply topology similarity coefficient and the power supply equipment similarity coefficient, and use the customer data similarity coefficient of customers corresponding to different common problems to determine whether to access the matching customer service; Use the customer data similarity coefficient of customers corresponding to different common problems to determine whether to access the matching customer service, specifically including: Determine data similar customers in the customers by using customer data similarity coefficients corresponding to different common problems, and determine that the matching customer service needs to be accessed when the number of the data similar customers is greater than a preset similar customer number.

2. The data analysis system for information customer care of claim 1, wherein, The power supply equipment of the customer includes a transformer, a transmission line, a CT, and a switching device.

3. The data analysis system for information customer care of claim 1, wherein, The customer service allocation processing of the current customer is performed, and specifically includes: Determine a predicted problem type on a current date based on the power supply equipment of the current customer, and perform the customer service allocation processing of the current customer by using the predicted problem type.

4. The data analysis system for information customer care of claim 3, wherein, The predicted problem type is determined according to a problem type of a similar historical customer of the power supply equipment of the current customer.

5. The data analysis system for information customer care of claim 1, wherein, The weather data includes a temperature, a humidity, and a wind speed.

6. The data analysis system for information customer care of claim 1, wherein, Different dates are divided into different similar date groups, and specifically includes: Determine a deviation coefficient of weather data between different dates in different dimensions based on the weather data between the different dates; Determine a weather data deviation coefficient between different dates according to an average value of the deviation coefficients of the weather data in different dimensions; Divide the different dates into the different similar date groups based on the weather data deviation coefficient.

7. The data analysis system for information customer care of claim 1, wherein, Divide the different dates into the different similar date groups based on the weather data deviation coefficient, and specifically includes: Divide dates with the weather data deviation coefficients in a preset interval into a same similar date group.

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