CN116151840B - User service data intelligent management system and method based on big data - Google Patents
User service data intelligent management system and method based on big data Download PDFInfo
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Abstract
The invention discloses an intelligent management system and method for user service data based on big data, and belongs to the technical field of internet delay data processing. The system comprises: the system comprises a service supervision module, an extraction analysis module, a front-end early warning module, a comprehensive evaluation module and an instruction analysis module; the output end of the service supervision module is connected with the input end of the extract analysis module; the output end of the extraction analysis module is connected with the input end of the pre-warning module; the output end of the pre-warning module is connected with the input end of the comprehensive evaluation module; the output end of the comprehensive evaluation module is connected with the input end of the instruction analysis module. The method and the device can improve user experience in the internet delay and protection process, and make delay and protection service for the user, and meanwhile solve the early warning problem in the internet delay and protection process.
Description
Technical Field
The invention relates to the technical field of internet delay data processing, in particular to an intelligent management system and method for user service data based on big data.
Background
The extended service is a service mode for continuing package repair after a three-package period. By extended warranty is meant a product purchased by a consumer (including tangible products and intangible products such as insurance, service, etc.), and paid for service by an extended warranty provider, or by extending the service area of the product, or by deriving the service, beyond the shelf life and service area provided by the manufacturer. The mode of the deferred service through the internet + will change the traditional business model. The service is provided through the Internet platform, and the user completes all processes of knowing, purchasing, paying and serving through the Internet.
However, in the existing internet extension service, although the convenience of the user is greatly improved, there is often a major defect in the actual extension service contract, and since the extension service is different from the service provider of the quality assurance service, the quality assurance service is generally provided by a product manufacturer, and the provider of the extension service has uncertainty, the product manufacturer, a channel, a third party maintenance enterprise, a third party extension company and the like may be all providers, so unnecessary trouble is often caused to the user. Early warning for such situations, and specific data management analysis for such situations, are also lacking.
Disclosure of Invention
The invention aims to provide a user service data intelligent management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a user service data intelligent management method based on big data includes the following steps:
s1, a user selects an extension service data input system, the system extracts keywords in the extension service data, the keywords comprise a location of the user and an extension responsible party, and the extension responsible party comprises a manufacturer Fang Yanbao and a seller extension;
s2, if the delay responsible party is detected to be sold Fang Yanbao, generating an instruction to a pre-warning module, calling the place where the user is located, and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located;
s3, constructing a comprehensive evaluation model, and respectively calculating evaluation scores of a third party maintenance mechanism and a seller or a third party maintenance mechanism appointed by the seller in the area where the user is located;
s4, respectively selecting the optimal value of the evaluation score for comparison analysis, and if the optimal value of the evaluation score of the third-party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, performing instruction elimination in the pre-warning module; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port.
According to the above technical scheme, in step S1, the manufacturer Fang Yanbao reports to the product manufacturer that repair is required when repair occurs during the delay period, and any third party repair mechanism designated by the manufacturer can perform repair treatment; the selling Fang Yanbao refers to that when maintenance occurs during the extended period, the maintenance needs to be reported to the seller of the product, and the maintenance is performed at the seller or a third party maintenance mechanism designated by the seller.
In actual life, some illegal merchants tend to blur relevant contents in the extended service contract, and most consumers do not know the distinction between quality assurance and extended assurance, so that the consumers tend to be the same as the extended service corresponding mechanisms and the quality assurance service corresponding mechanisms, thereby causing a lot of troubles in the follow-up.
According to the above technical scheme, the constructing the comprehensive evaluation model includes:
constructing a shop comprehensive evaluation influence factor of a third party maintenance mechanism, wherein the shop comprehensive evaluation influence factor comprises a distance from a user to a place, a business duration, a social network bad evaluation occupation ratio and a shop annual business amount;
the system sets an initial weight ratio, performs normalization data processing on the comprehensive evaluation influence factors, and then selects historical data of a third-party maintenance mechanism participating in training to construct a comprehensive evaluation model: y=k 1 x 1 +k 2 x 2 +k 3 x 3 +k 4 x 4 The method comprises the steps of carrying out a first treatment on the surface of the Wherein Y represents a composite score, k 1 、k 2 、k 3 、k 4 Respectively representing initial weight duty ratios; x is x 1 、x 2 、x 3 、x 4 Normalized values representing the distance from the user to the place, business duration, social network bad evaluation duty ratio and shop annual sales;
obtaining the selected number of times of the third party maintenance organization i as m i Obtaining ranking number L of mi in third-party maintenance institutions participating in training i Sequentially from large to small; obtaining a composite score Y of a third party maintenance institution i i Obtaining Y i Comprehensive score ranking number N at third party maintenance institutions involved in training i Sequentially from large to small; taking L i And N i If the absolute value of the difference exceeds the system set threshold, marking once; to all participationsProcessing by a trained third-party maintenance mechanism, and outputting total marking times;
constructing a tolerance function relationship between total number of third-party maintenance institutions participating in training and marking times, wherein k is 5 To influence the coefficients, u=k 5 *U 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is the influence threshold of the total marking times; u (U) 0 Total number of third party maintenance institutions involved in training; if the output total marking times exceed U, carrying out self-adaptive adjustment on the initial weight ratio, and repeating calculation after adjustment until the output total marking times do not exceed U, and outputting the current weight ratio as the weight ratio of the final comprehensive evaluation model.
In the above technical solution, the continuous adjustment is mainly adopted to ensure that the score value and the ranking value are in the same horizontal line, and only under such a condition, the weight ratio is reasonable, and of course, different weight ratios may exist or the value of the weight ratio is not fixed, but only the condition is required to be satisfied, namely the condition exists reasonably, and if the accuracy is required to be further processed, the system setting threshold value is required to be processed and updated again.
According to the above technical solution, in steps S3 to S4, further comprising:
respectively calculating the evaluation scores of the third-party maintenance institutions and the sellers or the third-party maintenance institutions appointed by the sellers in the area where the user is located according to the final comprehensive evaluation model;
respectively selecting the optimal values of the evaluation scores for comparison analysis;
the selecting of the optimal value of the evaluation score includes:
acquiring product service life historical data of maintenance products of different third-party maintenance institutions under historical data, and selecting the most product service life of all maintenance products of each third-party maintenance institution as the common maintenance life of the maintenance products of the third-party maintenance institutions;
obtaining the service life of a product of a user applying for internet delay service, and selecting the maximum value of the comprehensive evaluation score in a third party maintenance mechanism which is the same as the service life of the product of the user applying for internet delay service as an optimal value;
if the third party maintenance mechanism which is the same as the service life of the product of the Internet delay service applied by the user does not exist, directly selecting the maximum value of the comprehensive evaluation score in the third party maintenance mechanism as an optimal value;
respectively obtaining an optimal value of the evaluation score of the third-party maintenance institution designated by the seller or the seller and an optimal value of the evaluation score of the third-party maintenance institution in the area where the user is located;
in the above technical solution, the service life of the product of the internet service is applied by the user, for example, the service life of the product is 1 year, the service life of the product is required to be prolonged in the 2 nd year, then the fault condition of each product in different service life is different, generally, the faults of more important parts are gradually increased along with the gradual increase of the service life, and in different third party maintenance institutions, the fault degree which can be solved is different due to different body amounts or different technologies, so that the service life of the maintenance product of the third party maintenance institutions is combined with the service life of the user product to determine the optimal value, thereby improving the accuracy of subsequent alarm.
If the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, the pre-warning module is used for eliminating the instruction; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port.
An intelligent management system for user service data based on big data, the system comprising: the system comprises a service supervision module, an extraction analysis module, a front-end early warning module, a comprehensive evaluation module and an instruction analysis module;
the service supervision module is used for creating a service window and acquiring the delay service data input by the user; the extraction analysis module is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and delay responsible parties, and the delay responsible parties comprise factories Fang Yanbao and sellers delay; the pre-warning module is used for generating different instructions according to different extension responsible parties, generating instructions to the pre-warning module if the extension responsible party is detected to be sold Fang Yanbao, and calling the location of the user to acquire the position and evaluation data of a third-party maintenance mechanism in the area of the user; the comprehensive evaluation module is used for constructing a comprehensive evaluation model and respectively calculating evaluation scores of a third party maintenance mechanism in the area where the user is located and a seller or a third party maintenance mechanism appointed by the seller; the instruction analysis module is used for respectively selecting the optimal value of the evaluation score for comparison analysis, and if the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located, the instruction elimination is carried out in the pre-early warning module; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port;
the output end of the service supervision module is connected with the input end of the extract analysis module; the output end of the extraction analysis module is connected with the input end of the pre-warning module; the output end of the pre-warning module is connected with the input end of the comprehensive evaluation module; the output end of the comprehensive evaluation module is connected with the input end of the instruction analysis module.
According to the technical scheme, the service supervision module comprises a window input unit and a data processing unit;
the window input unit is used for creating a service window, and a user inputs the service window after acquiring the internet delay service contract; the data processing unit is used for storing the delay service contract of the input service window and sending the delay service contract to the extraction analysis module;
the output end of the window input unit is connected with the input end of the data processing unit.
According to the technical scheme, the snippet analysis module comprises a snippet analysis unit and a judging unit;
the extraction analysis unit is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and the delay responsible party; the judging unit is used for judging whether the delay responsible party is a manufacturer Fang Yanbao or a seller delay;
the output end of the extraction analysis unit is connected with the input end of the judging unit.
According to the technical scheme, the pre-warning module comprises an instruction generating unit and a calling unit;
the instruction generation unit is used for generating different instructions according to different delay responsible parties, and generating instructions to the pre-warning module if the delay responsible party is detected to be selling Fang Yanbao; the calling unit is used for calling the place where the user is located and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located;
the output end of the instruction generating unit is connected with the input end of the calling unit.
According to the technical scheme, the comprehensive evaluation module comprises a model construction unit and a calculation unit;
the model construction unit is used for constructing a comprehensive evaluation model; the computing unit is used for respectively computing the evaluation scores of the third-party maintenance institutions and the third-party maintenance institutions appointed by sellers or sellers in the area where the user is located according to the comprehensive evaluation model;
the output end of the model building unit is connected with the input end of the calculating unit.
According to the technical scheme, the instruction analysis module comprises a comparison analysis unit and an early warning classification unit;
the comparison analysis unit is used for respectively selecting the optimal values of the evaluation scores to carry out comparison analysis; the early warning classification unit is used for carrying out instruction elimination on a preposed early warning module when the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located; and converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to a user port, wherein the optimal value of the evaluation score of the third-party maintenance mechanism appointed by the seller or the seller is not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located.
Compared with the prior art, the invention has the following beneficial effects: the invention can improve the user experience in the internet delay and protection process, and make delay and protection service for the user, and simultaneously solve the early warning problem in the internet delay and protection process, improve the service capability of the internet delay and protection service, avoid the user from stepping on the mine when facing different delay and protection service providers, and ensure the legal rights and interests of consumers.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a system and method for intelligent management of user service data based on big data according to the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in a first embodiment: analyzing and managing user service data under the internet delay, wherein a user selects a delay service data input system, the system extracts keywords in the delay service data, the keywords comprise a user location and a delay responsible party, and the delay responsible party comprises a manufacturer Fang Yanbao and a seller delay; the manufacturer Fang Yanbao refers to that when maintenance occurs in the delay period, repair needs to be reported to a product manufacturer, and any third party maintenance mechanism designated by the manufacturer can perform maintenance treatment; the selling Fang Yanbao refers to that when maintenance occurs during the extended period, the maintenance needs to be reported to the seller of the product, and the maintenance is performed at the seller or a third party maintenance mechanism designated by the seller.
If the delay responsible party is detected to be sold Fang Yanbao, generating an instruction to a pre-warning module, calling the location of the user, and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located; constructing a comprehensive evaluation model, and respectively calculating evaluation scores of a third party maintenance mechanism and a seller or a third party maintenance mechanism appointed by the seller in the area where the user is located;
the building of the comprehensive evaluation model comprises the following steps:
constructing a shop comprehensive evaluation influence factor of a third party maintenance mechanism, wherein the shop comprehensive evaluation influence factor comprises a distance from a user to a place, a business duration, a social network bad evaluation occupation ratio and a shop annual business amount;
the sample data are extracted and sorted Internet extended service data are used as training sets and test sets, store comprehensive evaluation influence factors are provided with indexes such as distance between the store comprehensive evaluation influence factors and the user, business duration, social network difference evaluation proportion, store scale and the like, and when store comprehensive evaluation emotion analysis is carried out, the data such as the distance between the user and the store, business duration, social network difference evaluation proportion, store scale and the like are required to be selectively used from a database according to modeling requirements.
Sample data preprocessing is mainly speaker analysis processing of social network bad scores, and comprises missing value and repeated value processing, text extraction, word filtering disabling and the like.
The missing value and repeated value processing uses a pandas module in python to call dropna () to perform missing value processing on the data, and call drop_replicates () to perform repeated value processing on the data; the text extraction utilizes a re module to extract Chinese in comment data, and delete characters such as special characters, numbers and the like; the term "stop word" refers to a word having no influence on the emotion tendencies of analysis in more text data, such as "o", "earth" words. According to the online collection and summarization of stop words, a stop word list is created, the stop word list is utilized to remove useless words in text data, and the social network bad evaluation duty ratio is obtained after data preprocessing;
the comprehensive evaluation model is built, a snowNLP algorithm is adopted in the process of building the model, wherein the snowNLP is a python written class library, chinese text content can be conveniently processed, and unlike textBlob, NLTK is not used here, all algorithms are realized by themselves, and a plurality of trained dictionaries are provided.
The system sets an initial weight ratio, performs normalization data processing on the comprehensive evaluation influence factors, and then selects historical data of a third-party maintenance mechanism participating in training to construct a comprehensive evaluation model: y=k 1 x 1 +k 2 x 2 +k 3 x 3 +k 4 x 4 The method comprises the steps of carrying out a first treatment on the surface of the Wherein Y represents a composite score, k 1 、k 2 、k 3 、k 4 Respectively representing initial weight duty ratios; x is x 1 、x 2 、x 3 、x 4 Normalized values representing the distance from the user to the place, business duration, social network bad evaluation duty ratio and shop annual sales; the calculated composite evaluation score represents a positive probability, the closer to 0, the more negative the resulting performance, the closer to 1, and the more positive the resulting performance.
Obtaining the selected number of times of the third party maintenance organization i as m i Obtaining ranking number L of mi in third-party maintenance institutions participating in training i Sequentially from large to small; obtaining a composite score Y of a third party maintenance institution i i Obtaining Y i Comprehensive score ranking number N at third party maintenance institutions involved in training i Sequentially from large to small; taking L i And N i If the absolute value of the difference exceeds the system set threshold, marking once; processing all third-party maintenance institutions participating in training, and outputting total marking times;
constructing a tolerance function relationship between total number of third-party maintenance institutions participating in training and marking times, wherein k is 5 To influence the coefficients, u=k 5 *U 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is the influence threshold of the total marking times；U 0 Total number of third party maintenance institutions involved in training; if the output total marking times exceed U, carrying out self-adaptive adjustment on the initial weight ratio, and repeating calculation after adjustment until the output total marking times do not exceed U, and outputting the current weight ratio as the weight ratio of the final comprehensive evaluation model.
In steps S3-S4, further comprising:
respectively calculating the evaluation scores of the third-party maintenance institutions and the sellers or the third-party maintenance institutions appointed by the sellers in the area where the user is located according to the final comprehensive evaluation model;
respectively selecting the optimal values of the evaluation scores for comparison analysis;
the selecting of the optimal value of the evaluation score includes:
acquiring product service life historical data of maintenance products of different third-party maintenance institutions under historical data, and selecting the most product service life of all maintenance products of each third-party maintenance institution as the common maintenance life of the maintenance products of the third-party maintenance institutions;
obtaining the service life of a product of a user applying for internet delay service, and selecting the maximum value of the comprehensive evaluation score in a third party maintenance mechanism which is the same as the service life of the product of the user applying for internet delay service as an optimal value;
if the third party maintenance mechanism which is the same as the service life of the product of the Internet delay service applied by the user does not exist, directly selecting the maximum value of the comprehensive evaluation score in the third party maintenance mechanism as an optimal value;
respectively obtaining an optimal value of the evaluation score of the third-party maintenance institution designated by the seller or the seller and an optimal value of the evaluation score of the third-party maintenance institution in the area where the user is located;
if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, the pre-warning module is used for eliminating the instruction; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port.
In a second embodiment, an intelligent management system for user service data based on big data is provided, the system includes: the system comprises a service supervision module, an extraction analysis module, a front-end early warning module, a comprehensive evaluation module and an instruction analysis module;
the service supervision module is used for creating a service window and acquiring the delay service data input by the user; the extraction analysis module is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and delay responsible parties, and the delay responsible parties comprise factories Fang Yanbao and sellers delay; the pre-warning module is used for generating different instructions according to different extension responsible parties, generating instructions to the pre-warning module if the extension responsible party is detected to be sold Fang Yanbao, and calling the location of the user to acquire the position and evaluation data of a third-party maintenance mechanism in the area of the user; the comprehensive evaluation module is used for constructing a comprehensive evaluation model and respectively calculating evaluation scores of a third party maintenance mechanism in the area where the user is located and a seller or a third party maintenance mechanism appointed by the seller; the instruction analysis module is used for respectively selecting the optimal value of the evaluation score for comparison analysis, and if the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located, the instruction elimination is carried out in the pre-early warning module; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port;
the output end of the service supervision module is connected with the input end of the extract analysis module; the output end of the extraction analysis module is connected with the input end of the pre-warning module; the output end of the pre-warning module is connected with the input end of the comprehensive evaluation module; the output end of the comprehensive evaluation module is connected with the input end of the instruction analysis module.
The service supervision module comprises a window input unit and a data processing unit;
the window input unit is used for creating a service window, and a user inputs the service window after acquiring the internet delay service contract; the data processing unit is used for storing the delay service contract of the input service window and sending the delay service contract to the extraction analysis module;
the output end of the window input unit is connected with the input end of the data processing unit.
The extraction analysis module comprises an extraction analysis unit and a judgment unit;
the extraction analysis unit is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and the delay responsible party; the judging unit is used for judging whether the delay responsible party is a manufacturer Fang Yanbao or a seller delay;
the output end of the extraction analysis unit is connected with the input end of the judging unit.
The pre-warning module comprises an instruction generating unit and a calling unit;
the instruction generation unit is used for generating different instructions according to different delay responsible parties, and generating instructions to the pre-warning module if the delay responsible party is detected to be selling Fang Yanbao; the calling unit is used for calling the place where the user is located and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located;
the output end of the instruction generating unit is connected with the input end of the calling unit.
The comprehensive evaluation module comprises a model construction unit and a calculation unit;
the model construction unit is used for constructing a comprehensive evaluation model; the computing unit is used for respectively computing the evaluation scores of the third-party maintenance institutions and the third-party maintenance institutions appointed by sellers or sellers in the area where the user is located according to the comprehensive evaluation model;
the output end of the model building unit is connected with the input end of the calculating unit.
The instruction analysis module comprises a comparison analysis unit and an early warning classification unit;
the comparison analysis unit is used for respectively selecting the optimal values of the evaluation scores to carry out comparison analysis; the early warning classification unit is used for carrying out instruction elimination on a preposed early warning module when the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located; the optimal value of the evaluation score of the third-party maintenance mechanism appointed by the seller or the seller is not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, and the instruction of the pre-warning module is converted into warning prompt information and is output to the user port;
the output end of the comparison analysis unit is connected with the input end of the early warning classification unit.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A user service data intelligent management method based on big data is characterized in that: the method comprises the following steps:
s1, a user selects an extension service data input system, the system extracts keywords in the extension service data, the keywords comprise a location of the user and an extension responsible party, and the extension responsible party comprises a manufacturer Fang Yanbao and a seller extension;
s2, if the delay responsible party is detected to be sold Fang Yanbao, generating an instruction to a pre-warning module, calling the place where the user is located, and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located;
s3, constructing a comprehensive evaluation model, and respectively calculating evaluation scores of a third party maintenance mechanism and a seller or a third party maintenance mechanism appointed by the seller in the area where the user is located;
s4, respectively selecting the optimal value of the evaluation score for comparison analysis, and if the optimal value of the evaluation score of the third-party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, performing instruction elimination in the pre-warning module; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port;
the building of the comprehensive evaluation model comprises the following steps:
constructing a shop comprehensive evaluation influence factor of a third party maintenance mechanism, wherein the shop comprehensive evaluation influence factor comprises a distance from a user to a place, a business duration, a social network bad evaluation occupation ratio and a shop annual business amount;
the system sets an initial weight ratio, performs normalization data processing on the comprehensive evaluation influence factors, and then selects historical data of a third-party maintenance mechanism participating in training to construct a comprehensive evaluation model: y=k 1 x 1 +k 2 x 2 +k 3 x 3 +k 4 x 4 The method comprises the steps of carrying out a first treatment on the surface of the Wherein Y represents the composite score and wherein,k 1 、k 2 、k 3 、k 4 respectively representing initial weight duty ratios; x is x 1 、x 2 、x 3 、x 4 Normalized values representing the distance from the user to the place, business duration, social network bad evaluation duty ratio and shop annual sales; the calculated composite score represents a positive probability, the closer to 0, the more negative the resulting performance, the closer to 1, the more positive the resulting performance;
obtaining the selected number of times of the third party maintenance organization i as m i Obtaining ranking number L of mi in third-party maintenance institutions participating in training i Sequentially from large to small; obtaining a composite score Y of a third party maintenance institution i i Obtaining Y i Comprehensive score ranking number N at third party maintenance institutions involved in training i Sequentially from large to small; taking L i And N i If the absolute value of the difference exceeds the system set threshold, marking once; processing all third-party maintenance institutions participating in training, and outputting total marking times;
constructing a tolerance function relationship between total number of third-party maintenance institutions participating in training and marking times, wherein k is 5 To influence the coefficients, u=k 5 *U 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is the influence threshold of the total marking times; u (U) 0 Total number of third party maintenance institutions involved in training; if the output total marking times exceed U, carrying out self-adaptive adjustment on the initial weight ratio, and repeating calculation after adjustment until the output total marking times do not exceed U, and outputting the current weight ratio as the weight ratio of the final comprehensive evaluation model.
2. The intelligent management method for user service data based on big data according to claim 1, wherein: in step S1, the manufacturer Fang Yanbao needs to report repair to the product manufacturer when repair occurs during the delay period, and any third party repair mechanism designated by the manufacturer can perform repair treatment; the selling Fang Yanbao refers to that when maintenance occurs during the extended period, the maintenance needs to be reported to the seller of the product, and the maintenance is performed at the seller or a third party maintenance mechanism designated by the seller.
3. The intelligent management method for user service data based on big data according to claim 2, wherein: in steps S3-S4, further comprising:
respectively calculating the evaluation scores of the third-party maintenance institutions and the sellers or the third-party maintenance institutions appointed by the sellers in the area where the user is located according to the final comprehensive evaluation model;
respectively selecting the optimal values of the evaluation scores for comparison analysis;
the selecting of the optimal value of the evaluation score includes:
acquiring product service life historical data of maintenance products of different third-party maintenance institutions under historical data, and selecting the most product service life of all maintenance products of each third-party maintenance institution as the common maintenance life of the maintenance products of the third-party maintenance institutions;
obtaining the service life of a product of a user applying for internet delay service, and selecting the maximum value of the comprehensive evaluation score in a third party maintenance mechanism which is the same as the service life of the product of the user applying for internet delay service as an optimal value;
if the third party maintenance mechanism which is the same as the service life of the product of the Internet delay service applied by the user does not exist, directly selecting the maximum value of the comprehensive evaluation score in the third party maintenance mechanism as an optimal value;
respectively obtaining an optimal value of the evaluation score of the third-party maintenance institution designated by the seller or the seller and an optimal value of the evaluation score of the third-party maintenance institution in the area where the user is located;
if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, the pre-warning module is used for eliminating the instruction; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port.
4. The intelligent management system for user service data based on big data applying the intelligent management method for user service data based on big data according to claim 1, wherein the intelligent management system for user service data based on big data is characterized in that: the system comprises: the system comprises a service supervision module, an extraction analysis module, a front-end early warning module, a comprehensive evaluation module and an instruction analysis module;
the service supervision module is used for creating a service window and acquiring the delay service data input by the user; the extraction analysis module is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and delay responsible parties, and the delay responsible parties comprise factories Fang Yanbao and sellers delay; the pre-warning module is used for generating different instructions according to different extension responsible parties, generating instructions to the pre-warning module if the extension responsible party is detected to be sold Fang Yanbao, and calling the location of the user to acquire the position and evaluation data of a third-party maintenance mechanism in the area of the user; the comprehensive evaluation module is used for constructing a comprehensive evaluation model and respectively calculating evaluation scores of a third party maintenance mechanism in the area where the user is located and a seller or a third party maintenance mechanism appointed by the seller; the instruction analysis module is used for respectively selecting the optimal value of the evaluation score for comparison analysis, and if the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located, the instruction elimination is carried out in the pre-early warning module; if the seller or the third-party maintenance mechanism appointed by the seller has the optimal value of the evaluation score not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, converting the instruction of the pre-warning module into warning prompt information and outputting the warning prompt information to the user port;
the output end of the service supervision module is connected with the input end of the extract analysis module; the output end of the extraction analysis module is connected with the input end of the pre-warning module; the output end of the pre-warning module is connected with the input end of the comprehensive evaluation module; the output end of the comprehensive evaluation module is connected with the input end of the instruction analysis module.
5. The intelligent management system for user service data based on big data according to claim 4, wherein: the service supervision module comprises a window input unit and a data processing unit;
the window input unit is used for creating a service window, and a user inputs the service window after acquiring the internet delay service contract; the data processing unit is used for storing the delay service contract of the input service window and sending the delay service contract to the extraction analysis module;
the output end of the window input unit is connected with the input end of the data processing unit.
6. The intelligent management system for user service data based on big data according to claim 4, wherein: the extraction analysis module comprises an extraction analysis unit and a judgment unit;
the extraction analysis unit is used for automatically extracting keywords in the delay service data, wherein the keywords comprise the place where the user is located and the delay responsible party; the judging unit is used for judging whether the delay responsible party is a manufacturer Fang Yanbao or a seller delay;
the output end of the extraction analysis unit is connected with the input end of the judging unit.
7. The intelligent management system for user service data based on big data according to claim 4, wherein: the pre-warning module comprises an instruction generating unit and a calling unit;
the instruction generation unit is used for generating different instructions according to different delay responsible parties, and generating instructions to the pre-warning module if the delay responsible party is detected to be selling Fang Yanbao; the calling unit is used for calling the place where the user is located and acquiring the position and evaluation data of a third party maintenance mechanism in the area where the user is located;
the output end of the instruction generating unit is connected with the input end of the calling unit.
8. The intelligent management system for user service data based on big data according to claim 4, wherein: the comprehensive evaluation module comprises a model construction unit and a calculation unit;
the model construction unit is used for constructing a comprehensive evaluation model; the computing unit is used for respectively computing the evaluation scores of the third-party maintenance institutions and the third-party maintenance institutions appointed by sellers or sellers in the area where the user is located according to the comprehensive evaluation model;
the output end of the model building unit is connected with the input end of the calculating unit.
9. The intelligent management system for user service data based on big data according to claim 4, wherein: the instruction analysis module comprises a comparison analysis unit and an early warning classification unit;
the comparison analysis unit is used for respectively selecting the optimal values of the evaluation scores to carry out comparison analysis; the early warning classification unit is used for carrying out instruction elimination on a preposed early warning module when the optimal value of the evaluation score of the third party maintenance mechanism appointed by the seller or the seller is higher than the optimal value of the evaluation score of the third party maintenance mechanism in the area where the user is located; the optimal value of the evaluation score of the third-party maintenance mechanism appointed by the seller or the seller is not higher than the optimal value of the evaluation score of the third-party maintenance mechanism in the area where the user is located, and the instruction of the pre-warning module is converted into warning prompt information and is output to the user port;
the output end of the comparison analysis unit is connected with the input end of the early warning classification unit.
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Denomination of invention: A user service data intelligent management system and method based on big data Effective date of registration: 20230907 Granted publication date: 20230711 Pledgee: Chengdong Branch of Nanjing Bank Co.,Ltd. Pledgor: Nanjing Shuce Information Technology Co.,Ltd. Registration number: Y2023980055726 |