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CN118134503B - After-sales service management method, after-sales service management device and storage medium - Google Patents

After-sales service management method, after-sales service management device and storage medium Download PDF

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CN118134503B
CN118134503B CN202410559297.9A CN202410559297A CN118134503B CN 118134503 B CN118134503 B CN 118134503B CN 202410559297 A CN202410559297 A CN 202410559297A CN 118134503 B CN118134503 B CN 118134503B
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reference value
information
work order
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CN118134503A (en
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阳杰
沈焕元
曾绍兵
谢海涛
林宋勉
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Guangzhou Pingyun Xiaojiang Technology Co ltd
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Abstract

The application discloses an after-sales service management method, equipment and a storage medium, and relates to the technical field of general control systems, wherein the after-sales service management method comprises the following steps: when an after-sales work order is received, extracting after-sales information contained in the after-sales work order; determining a first reference value based on the maintainer evaluation corresponding to the manufacturer information in the after-sales information; performing weighted calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, and determining a second reference value; generating a reference value sequence based on historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address; and inputting the first reference value, the second reference value and the reference value sequence into a decision model and at least one reference model, and determining a target maintenance person corresponding to the after-sales work order. The application can realize the quick response and accurate matching of the after-sales worksheets to the after-sales demands, and improves the technical effect of the after-sales service quality of small and micro enterprises.

Description

After-sales service management method, after-sales service management device and storage medium
Technical Field
The present application relates to the field of general control systems, and in particular, to a method, apparatus, and storage medium for after-sales service management.
Background
Currently, depending on the e-commerce platform and the improvement of the capacity of national logistics, the commodities of many small micro factories can be sold to various places, but when consumers purchase products, especially electronic consumer products, convenient after-sales service affects the brand selection of the consumers to a great extent.
Traditional after-sales service is usually carried out on line, after-sales service is applied for by customers through telephones, after-sales service personnel receive the after-sales application and inform maintenance personnel, the maintenance personnel are contacted with the customers to carry out specific after-sales service, namely after-sales information needs to be transferred through multiple levels, in order to meet after-sales aging, manufacturers relying on products need to have after-sales sites covering the whole area, but a small micro-factory cannot bear cost expense brought by the whole area after-sales sites.
Therefore, how to improve the after-sales service quality of small micro enterprises is a problem to be solved.
Disclosure of Invention
The application mainly aims to provide an after-sales service management method, equipment and a storage medium, which aim to solve the technical problem of how to improve the after-sales service quality of small and micro enterprises.
In order to achieve the above object, the present application provides an after-sales service management method applied to an after-sales service management system, wherein the after-sales service management system stores manufacturer information corresponding to a manufacturer accessed in advance and a historical after-sales work order, and the after-sales service management method includes:
When an after-sales work order is received, extracting after-sales information contained in the after-sales work order based on character recognition and a work order template corresponding to the after-sales work order;
Determining a first reference value based on a maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer;
Performing weighted calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, and determining a second reference value;
generating a reference value sequence based on historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address;
Acquiring historical after-sales information, wherein the historical after-sales information comprises the fault type and a maintenance result;
Analyzing an influence factor of the fault type on the maintenance result for each equipment type;
According to the influence factors, integrating all the historical after-sales information, and determining the influence weight of the fault type on the maintenance result;
generating a decision model based on the impact weight;
And inputting the first reference value, the second reference value and the reference value sequence into the decision model and at least one reference model, and determining a target maintenance person corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model.
In an embodiment, the step of extracting after-sales information included in the after-sales work order based on word recognition and a work order template corresponding to the after-sales work order when the after-sales work order is received includes:
Determining the work order template based on the work order identification of the after-sales work order;
acquiring coordinate information of each content unit in the work order template on target paper;
partitioning the after-market worksheet into the content units based on the coordinate information;
and extracting the content unit based on the character recognition, and determining the after-sales information.
In an embodiment, the step of determining the first reference value based on the serviceman evaluation corresponding to the manufacturer information in the after-sales information includes:
acquiring the historical after-sales work order of the fault type in the after-sales information corresponding to the manufacturer information;
acquiring the evaluation information corresponding to each maintainer in the historical after-sales work order;
And determining the first reference value based on a first mapping score corresponding to the evaluation information and the rating of the maintenance personnel.
In an embodiment, the step of determining the first reference value based on the first mapping score corresponding to the evaluation information and the rating of the maintenance personnel includes:
Determining the first mapping score based on the evaluation score in the evaluation information and the semantic recognition result corresponding to the evaluation information;
Taking the rating of the maintenance personnel as the weight of the rating information corresponding to the maintenance personnel;
and carrying out weighted calculation based on the weight of the evaluation information and the first mapping score to determine the first reference value.
In an embodiment, the step of determining the second reference value based on the weighted calculation of the device type, the fault type and the after-market address in the after-market information includes:
determining the equipment type and determining first maintenance difficulty;
determining a second repair difficulty based on the first repair difficulty and the fault type;
determining third maintenance difficulty based on the area level corresponding to the after-sales address;
Determining a weight sequence based on the manufacturer information;
and carrying out weighted calculation according to the weight sequence, the first maintenance difficulty, the second maintenance difficulty and the third maintenance difficulty, and determining the second reference value.
In an embodiment, the step of inputting the first reference value, the second reference value, and the reference value sequence to a decision model and at least one reference model, and determining a target serviceman corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model includes:
Inputting the first reference value, the second reference value and the reference value sequence into the decision model and at least one reference model, and determining the first matching result corresponding to the decision model and the second matching result corresponding to the reference model;
and determining the target maintenance personnel according to the first matching result, the first trust weight corresponding to the decision model, the second matching result and the second trust weight corresponding to the reference model.
In an embodiment, the step of determining the target maintenance person according to the first matching result, the first trust weight corresponding to the decision model, the second matching result, and the second trust weight corresponding to the reference model includes:
acquiring a third trust weight corresponding to each reference model;
Determining a reference matching sequence corresponding to the maintenance personnel based on the third trust weight and the reference matching rate corresponding to each maintenance personnel in the second matching result;
determining maintenance personnel to be selected based on a coincidence list of the decision matching sequence corresponding to the maintenance personnel and the reference matching sequence in the first matching result;
And determining the target maintenance personnel based on the decision matching rate of the maintenance personnel to be selected, the reference matching sequence, the first trust weight and the second trust weight.
In addition, in order to achieve the above object, the present application also provides an after-sales service management apparatus including: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program configured to implement the steps of the after-market service management method as described above.
In addition, in order to achieve the above object, the present application also provides a storage medium that is a computer-readable storage medium having stored thereon a program that implements an after-sales service management method, the program that implements the after-sales service management method being executed by a processor to implement the steps of the after-sales service management method as described above.
The application provides an after-sales service management method, which comprises the steps of firstly storing manufacturer information of each manufacturer and a corresponding historical after-sales work order into an after-sales service management system, and then extracting after-sales information contained in the after-sales work order, namely the after-sales work order can be an electronic version or a paper version, so that the compatibility and the processing efficiency of after-sales service are improved, and manual input of the work order is not needed; then, determining a first reference value based on maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer; performing weighted calculation based on the equipment type, the fault type and the after-sales address in the after-sales information, determining a second reference value, namely preprocessing the after-sales information contained in the after-sales work order, integrating according to the commonality and the difference between the data, and determining the reference value representing the after-sales requirement; generating a reference value sequence based on the historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address, namely determining the reference value sequence representing the service capacity of the maintenance personnel; and inputting the first reference value, the second reference value and the reference value sequence into a decision model and at least one reference model, determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model, determining a matching rate based on the decision model according to the reference value representing after-sales requirements and the reference value sequence representing service capacity of the maintainer, and correcting based on the reference model, thereby determining the maintainer with the highest matching rate as the target maintainer, and realizing quick response and accurate matching of the after-sales work order corresponding to the after-sales requirements.
In summary, the after-sales service resources of the coverage universe are integrated, each manufacturer is accessed to serve as an intermediate processing module between a consumer and the manufacturer, after-sales requirements corresponding to the after-sales worksheets and after-sales service capacities of maintenance personnel in corresponding areas are matched based on multiple dimensions, and therefore the technical effects of quick response and accurate matching of the after-sales requirements corresponding to the after-sales worksheets are achieved, and after-sales service quality of small micro enterprises is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of an after-sales service management method according to an embodiment of the present application;
FIG. 2 is a flow chart of a second embodiment of an after-sales service management method according to the present application;
FIG. 3 is a flowchart of steps S321-S324 in a third embodiment of an after-sales service management method according to the present application;
fig. 4 is a schematic diagram of a hardware structure related to an embodiment of an after-sales service management device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.
For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.
The main solution of the application is as follows: when an after-sales work order is received, extracting after-sales information contained in the after-sales work order based on character recognition and a work order template corresponding to the after-sales work order; determining a first reference value based on a maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer; performing weighted calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, and determining a second reference value; generating a reference value sequence based on historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address; and inputting the first reference value, the second reference value and the reference value sequence into a decision model and at least one reference model, and determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model.
At present, the small micro-factories cannot bear the cost expense brought by the global after-sale network point, so that the consumer has longer processing time when in after-sale and is not matched with the maintenance personnel meeting the requirements, and therefore, how to improve the after-sale service quality of the small micro-enterprises becomes a problem to be solved.
According to the application, after-sales service resources covering the universe are integrated, each manufacturer is accessed to serve as an intermediate processing module between a consumer and the manufacturer, after-sales requirements corresponding to the after-sales worksheets and after-sales service capacities of maintenance personnel in corresponding areas are matched based on multiple dimensions, and therefore, the technical effects of quick response and accurate matching of the after-sales requirements corresponding to the after-sales worksheets are realized, and the after-sales service quality of small micro enterprises is improved.
The execution subject of the present embodiment may be an after-sales service management system, or may be a computing service device having functions of data processing, network communication, and program running, such as a tablet computer, a personal computer, a mobile phone, or an after-sales service management device capable of implementing the above functions, which is not particularly limited in this embodiment. The present embodiment and the following embodiments will be described below using an after-sales service management system as an execution subject.
Based on this, the present application proposes an after-sales service management method according to a first embodiment, referring to fig. 1, the after-sales service management method includes steps S110 to S150:
And step S110, when receiving the after-sales work order, extracting after-sales information contained in the after-sales work order based on the character recognition and the work order template corresponding to the after-sales work order.
In this embodiment, the method is applied to an after-sales service management system, and the after-sales service management system stores manufacturer information corresponding to a pre-accessed manufacturer and a historical after-sales work order. Namely, receiving the after-sales work order through the after-sales service management system and matching the corresponding target maintenance personnel. The after-sales worksheets can be electronic or paper, but the worksheet templates corresponding to different worksheet identifications are predetermined. The work order template prescribes the coordinates of each content unit on the preset standard paper and the corresponding information type of each content unit in the after-sales work order.
In an after-sales service management system, a database is established for storing pre-accessed manufacturer information and historical after-sales work orders. The manufacturer information includes manufacturer name, contact, after-market rating, etc. The historical after-sales worksheets record after-sales worksheet information processed in the past, including worksheet identification, device type, fault type, after-sales address, rating, and the like.
As an alternative embodiment, step S110 includes: when an electronic version or a scanning version of an after-sales work order is received, the work order identification at a preset position on the after-sales work order is identified, a work order template corresponding to the after-sales work order is determined according to the work order identification, and attributes corresponding to each filling frame in the after-sales work order are determined according to the work order template, wherein the attributes comprise, but are not limited to, customer names, after-sales addresses, equipment types, fault types and the like. And extracting filling content corresponding to each filling frame based on the character recognition, and determining after-sales information corresponding to the after-sales work order based on the filling content and the attribute.
As another alternative embodiment, step S110 includes: determining the work order template based on the work order identification of the after-sales work order; acquiring coordinate information of each content unit in the work order template on target paper; partitioning the after-market worksheet into the content units based on the coordinate information; and extracting the content unit based on the character recognition, and determining the after-sales information.
In the present embodiment, the target paper is a paper of a prescribed size, such as A4 paper or A3 paper, and the size of the target paper is not particularly limited here.
Illustratively, the work order templates are determined, and corresponding work order templates are selected from an existing work order template library according to the work order identifications of the after-sales work orders. A worksheet template is a predefined layout that includes various information fields and their locations on the sheet. And loading the selected work order template into a system, and acquiring the coordinate information of each content unit in the work order template on the target paper through an image processing technology. By computing the bounding box of each content unit on the target sheet or finding its particular pixel pattern. Based on the obtained coordinate information, the received after-sales work order image is divided into individual content units. The position of each content unit is determined from the coordinate information by clipping the image. For each content unit, it is converted into recognizable text using appropriate word recognition techniques. Text in the work order is processed using optical character recognition to convert text in the image into editable text. And extracting the required after-sales information from the identified text according to the fields defined in the work order template. By using text matching algorithms, regular expressions, or other similar techniques. And further processing, cleaning or verifying the extracted information according to the requirement of the after-sale work order. The extracted after-sale information is further processed and perfected according to the requirements of clients and products, and the operations comprise data formatting, data cleaning, error correction, information association and the like. Finally, the extracted after-sale information is saved to a proper storage medium or transmitted to a subsequent business process.
The above are merely two possible implementations of step S110 provided in this embodiment, and the specific implementation of step S110 in this embodiment is not specifically limited.
And step S120, determining a first reference value based on the maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is the evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer.
In this embodiment, the first reference value is an abstract reference value based on historical evaluation information corresponding to the manufacturer in the after-sales information, and is used to represent the approval degree of the after-sales maintenance of the manufacturer in the maintenance personnel. Further considering that after-sales service is a two-way choice for consumers and maintenance personnel, when the corresponding evaluation information of the manufacturer is lower than a preset threshold, for example forty percent of full, the evaluation information is considered to be fed back to the manufacturer to prompt the manufacturer to correct.
As an alternative embodiment, based on the manufacturer information in the after-sales information, a historical after-sales work order record of the corresponding manufacturer is searched, and in the historical after-sales work order, a maintenance personnel evaluation of the after-sales address related maintenance personnel in the after-sales information is found. And generating a first reference value according to the evaluation information of the maintenance personnel to the manufacturer, and determining the first reference value by calculating the average value or the weighted average value of the evaluation scores of the maintenance personnel in the evaluation information.
As another alternative embodiment, step S120 includes:
Step S121, obtaining the historical after-sales work order of the failure type in the after-sales information corresponding to the manufacturer information.
In this embodiment, a historical after-sales work order corresponding to manufacturer information is obtained, and according to the manufacturer information in the after-sales information, a historical after-sales work order record corresponding to the manufacturer is obtained from a database of an after-sales service management system.
Step S122, obtaining the evaluation information corresponding to each maintenance person in the historical after-sales worksheet.
In this embodiment, the evaluation information corresponding to each serviceman is obtained, and in the history after-sales work order record, the evaluation information corresponding to each serviceman is found, including the evaluation score and the problem feedback of the serviceman to the history after-sales work order of the manufacturer.
Step S123, determining the first reference value based on the first mapping score corresponding to the evaluation information and the rating of the maintenance personnel.
In this embodiment, for each piece of evaluation information, a first mapping score corresponding to the evaluation information is calculated according to the rank or score thereof, and the calculation is performed using a rule engine or algorithm. For example, a mapping table is defined that maps the ratings to first mapping scores, or by calculating the first mapping scores based on a range of evaluation scores. Based on the evaluation information of the maintenance personnel and the corresponding first mapping score, a first reference value of each maintenance personnel is calculated, and the first reference value is realized through weighted average of the evaluation scores, wherein the weight is the first mapping score.
Optionally, step S123 includes: determining the first mapping score based on the evaluation score in the evaluation information and the semantic recognition result corresponding to the evaluation information; taking the rating of the maintenance personnel as the weight of the rating information corresponding to the maintenance personnel; and carrying out weighted calculation based on the weight of the evaluation information and the first mapping score to determine the first reference value.
In this embodiment, the evaluation information includes the evaluation content of the maintenance personnel on the post-sale worksheet of the manufacturer corresponding to the manufacturer information, including the evaluation score and the problem feedback of the maintenance personnel on the post-sale worksheet of the manufacturer.
Illustratively, a first mapping score is determined based on the evaluation score and the semantic recognition result, and for each piece of evaluation information, the evaluation information is analyzed and processed by the semantic recognition technology in consideration of the evaluation score. This may include extracting keywords or topics in the rating information using natural language processing algorithms and mapping them to corresponding first mapping scores using a predefined rules engine. For example, if the rating information includes keywords or topics related to quality of service, a higher first mapping score may be assigned. The rating of the maintenance personnel is used as the weight of the corresponding evaluation information. Ratings generally represent the comprehensive ability and performance of maintenance personnel in terms of technology and service. Higher ratings mean more reliable and better quality maintenance personnel. Taking the rating as a weight may ensure that higher rated serviceman's evaluation information is more emphasized when calculating the first reference value. And carrying out weighted calculation according to the weight of the evaluation information and the corresponding first mapping score so as to determine a first reference value. The final first reference value is determined according to a desired weight calculation method, such as simple average weights, weighted sums, etc. The weight calculation method should be chosen according to the actual requirements and objectives to ensure a comprehensive trade-off of the rating information and the first mapping score.
In this embodiment, the semantic recognition technology may select appropriate algorithms and tools according to the actual situation.
The above are merely two possible implementations of step S120 provided in this embodiment, and the specific implementation of step S120 in this embodiment is not specifically limited.
Step S130, performing a weighted calculation based on the device type, the fault type and the after-sales address in the after-sales information, and determining a second reference value.
In this embodiment, the second reference value is determined based on the device type, the fault type, and the after-market address in the after-market information, and is a reference value characterizing the after-market demand of the consumer.
And carrying out weight calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, generating a second reference value, and determining the second reference value by assigning a weight to each after-sale information attribute, multiplying the weight by the value of the corresponding attribute and then adding the weighted values of all the attributes.
And step S140, generating a reference value sequence based on the historical maintenance data corresponding to the maintenance personnel corresponding to the after-sale address.
In this embodiment, the sequence of reference values includes at least one serviceman reference value for characterizing the after-market service capabilities of serviceman from different dimensions.
According to the historical maintenance data of the maintenance personnel corresponding to the after-sales address, a reference value sequence is generated, and indexes such as average maintenance time, maintenance success rate and the like of the maintenance personnel corresponding to the after-sales address are calculated by analyzing the maintenance records in the historical maintenance data and are used as part of the reference value sequence.
Step S150, acquiring historical after-sale information, wherein the historical after-sale information comprises the fault type and maintenance results.
Step S160, analyzing the influence factors of the fault types on the maintenance results for each equipment type.
And S170, according to the influence factors, integrating all the historical after-sales information, and determining the influence weight of the fault type on the maintenance result.
Step S180, based on the influence weight, generating a decision model.
As an alternative embodiment, historical after-market information is collected and consolidated, including fault type and repair results for each equipment type. For example, the fault type (e.g., battery fault, screen fault, etc.) and corresponding repair results (e.g., repair, replacement, etc.) for each device type may be collected. And then analyzing the influence factors of the fault types on the maintenance results, and analyzing the historical after-sales information for each equipment type to determine the factors influencing the maintenance results. Consider the following factors: skill level and experience of maintenance personnel: and determining the influence of factors such as professional background, training level, working experience and the like of maintenance personnel on the maintenance result. The supply condition of the spare parts required for maintenance is determined, and the supply condition comprises the storage quantity, the supply period and other factors. The characteristics of the equipment are determined, including the influence of factors such as service life, vulnerable parts and the like on the fault type and maintenance result.
And further determining the influence weight of the fault type on the maintenance result, and determining the influence weight of each factor on the maintenance result by using an expert opinion or a statistical method based on the analyzed influence factors. And for the fault type of each equipment type, comprehensively considering the weights of all the influence factors according to the weights of the influence factors to obtain the influence weight of the fault type on the maintenance result. And finally, generating a decision model, and establishing the decision model based on the influence weight of the fault type on the maintenance result. Methods such as probabilistic models, statistical models, or machine learning models can be used. The decision model is trained using the after-market information and impact weights with the fault type as an input variable and the maintenance result as an output variable.
Step S190, inputting the first reference value, the second reference value and the reference value sequence to the decision model and at least one reference model, and determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model.
In this embodiment, the first reference value, the second reference value and the sequence of reference values are input to the decision model and the at least one reference model. The decision model is a model based on a machine learning algorithm or rules engine for determining the target service personnel based on the entered reference values. The reference model is a pattern matching model or recommendation system for recommending appropriate maintenance personnel based on the entered reference values. And determining a target maintenance person corresponding to the after-sales work order according to the first matching result of the decision model and the second matching result of the reference model, and determining the target maintenance person according to the ranking or the threshold value of the matching score. Further processing and perfecting can be carried out on the target maintenance personnel, including operations such as verification, screening, scheduling and the like.
As an alternative embodiment, the reference model is a trained neural network model, that is, based on the first reference value, the second reference value and the reference value sequence as input, and the matching rate corresponding to each maintainer is taken as output. But different reference models have different processing weights for the three input data of the first reference value, the second reference value and the reference value sequence, so that different reference models have different output results.
As another alternative embodiment, the reference models are different models, for example, four reference models are respectively a random forest model, a support vector machine model, a neural network model and a time sequence analysis model.
As another alternative implementation mode, when the reference model and the decision model are both neural network models, after the decision model is generated, the decision model is formed by integrating all the historical after-sales information by influence factors, and the influence weight of the fault type on the maintenance result is determined; and generating a decision model based on the impact weight. At this time, determining the model weight of the reference model, updating the influence weight of the fault type on the maintenance result based on the model weight, namely, the reference influence weight is the product of the model weight and the influence weight, and further generating the reference model according to the reference influence weight.
The embodiment provides an after-sales service management method, which comprises the steps of firstly storing manufacturer information of each manufacturer and a corresponding historical after-sales work order into an after-sales service management system, and then extracting after-sales information contained in the after-sales work order based on word recognition and a work order template corresponding to the after-sales work order when the after-sales work order is received, wherein the after-sales work order can be an electronic version or a paper version, so that compatibility and processing efficiency of after-sales service are improved, and manual input of the work order is not needed; then, determining a first reference value based on maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer; performing weighted calculation based on the equipment type, the fault type and the after-sales address in the after-sales information, determining a second reference value, namely preprocessing the after-sales information contained in the after-sales work order, integrating according to the commonality and the difference between the data, and determining the reference value representing the after-sales requirement; generating a reference value sequence based on the historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address, namely determining the reference value sequence representing the service capacity of the maintenance personnel; and inputting the first reference value, the second reference value and the reference value sequence into a decision model and at least one reference model, determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model, determining a matching rate based on the decision model according to the reference value representing after-sales requirements and the reference value sequence representing service capacity of the maintainer, and correcting based on the reference model, thereby determining the maintainer with the highest matching rate as the target maintainer, and realizing quick response and accurate matching of the after-sales work order corresponding to the after-sales requirements.
In summary, the after-sales service resources of the coverage universe are integrated, each manufacturer is accessed to serve as an intermediate processing module between a consumer and the manufacturer, after-sales requirements corresponding to the after-sales worksheets and after-sales service capacities of maintenance personnel in corresponding areas are matched based on multiple dimensions, and therefore the technical effects of quick response and accurate matching of the after-sales requirements corresponding to the after-sales worksheets are achieved, and after-sales service quality of small micro enterprises is improved.
In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the description above, and will not be repeated. On this basis, referring to fig. 2, step S130 includes:
Step S210, determining the equipment type to determine a first maintenance difficulty.
In this embodiment, the maintenance difficulty corresponding to different devices is different, for example, the air conditioner is generally installed outdoors, and the television is generally installed indoors, so that the maintenance difficulty of the air conditioner is generally greater than that of the television. Namely, the equipment type is preset with corresponding maintenance difficulty, and the corresponding first maintenance difficulty is obtained based on the equipment type corresponding to the after-sales work order.
Further, the first maintenance difficulty is not only determined uniquely according to the equipment type, but also can be referred to based on the equipment type and the after-sales address corresponding to the after-sales work order, semantic identification and map matching are performed on the after-sales address, the corresponding correction weight is determined based on floor information or passenger flow information corresponding to the after-sales address, and the first maintenance difficulty corresponding to the equipment type is updated based on the correction weight. Wherein, the higher the floor is, the greater the maintenance difficulty, and the more the passenger flow volume is, the greater the maintenance difficulty.
Step S220, determining a second repair difficulty based on the first repair difficulty and the fault type.
In this embodiment, after determining the first maintenance difficulty, the second maintenance difficulty is determined in combination with the failure type in the after-sales information and the first maintenance difficulty.
Since the first maintenance difficulty is a predetermined value or level, such as first gear, second gear, etc. Therefore, according to the first maintenance difficulty and the fault type, the second maintenance difficulty matched with the first maintenance difficulty and the fault type is determined based on a preset mapping relation.
And step S230, determining third maintenance difficulty based on the area level corresponding to the after-sales address.
In this embodiment, the regional level is an administrative level of the region where the after-market address is located, such as a city, a district, a county, a town, and the like. The higher the area level, the more abundant the maintenance personnel resources are, and the lower the maintenance difficulty is.
As an alternative implementation manner, semantic recognition is carried out on after-sales addresses in after-sales information, the area level corresponding to the after-sales addresses is determined, the maintenance difficulty corresponding to the area level is obtained, the number of maintenance personnel resources of the area corresponding to the after-sales addresses is obtained, and the third maintenance difficulty is determined based on the number of the maintenance personnel resources and the maintenance difficulty corresponding to the area level.
And step S240, determining a weight sequence based on the manufacturer information.
In this embodiment, each manufacturer is pre-adapted with weights for three maintenance difficulties, i.e., a first maintenance difficulty, a second maintenance difficulty and a third maintenance difficulty, respectively, i.e., a weight sequence has three values, and the three maintenance difficulties respectively correspond to each other.
Step S250, performing weighted calculation according to the weight sequence, the first maintenance difficulty, the second maintenance difficulty and the third maintenance difficulty, and determining the second reference value.
In this embodiment, based on the weight sequence, the first repair weight, the second repair weight and the third repair weight are determined, weighting calculation is performed according to the first repair weight and the first repair difficulty, the second repair difficulty and the second repair weight, and the third repair weight and the third repair difficulty, and the second reference value is determined according to the calculation result.
Determining a first maintenance difficulty due to determining the device type; determining a second repair difficulty based on the first repair difficulty and the fault type; determining third maintenance difficulty based on the area level corresponding to the after-sales address; determining a weight sequence based on the manufacturer information; and carrying out weighted calculation according to the weight sequence, the first maintenance difficulty, the second maintenance difficulty and the third maintenance difficulty, and determining the second reference value. And further, the after-sales requirements corresponding to the after-sales worksheets are accurately abstracted, and the technical effect of improving the matching accuracy of the after-sales worksheets and maintenance personnel is achieved.
In the third embodiment of the present application, the same or similar contents as those of the first and second embodiments of the present application can be referred to the description above, and the description thereof will not be repeated. On this basis, step S150 includes:
step S310, inputting the first reference value, the second reference value, and the reference value sequence to the decision model and at least one of the reference models, and determining the first matching result corresponding to the decision model and the second matching result corresponding to the reference model.
In this embodiment, there is one decision model, and at least one reference model, i.e., based on multiple model cross decisions. The matching results are the matching rate and ranking of the individual servicemen of the model output for the after-market worksheet. The higher the matching rate is, the more the requirements of the maintenance personnel and the after-sales work order are met, and the stronger the maintenance will of the product is carried out by the maintenance personnel. That is, in this embodiment, the target maintenance person is determined based on the two-way selection of the after-sales work order and the maintenance person. The first matching result is a matching result output by the decision model, and the number of the first matching results is one; the second matching result is a matching result output by the reference model, and a plurality of second matching results can be obtained.
As an optional implementation manner, inputting a first reference value, a second reference value and a reference value sequence into a decision model, and determining a first matching result corresponding to the decision model; and inputting the first reference value, the second reference value and the reference value sequence into at least one reference model, and determining a second matching result corresponding to each reference model.
As another alternative implementation manner, the first reference value, the second reference value and the reference value sequence are input into the decision model, and a first matching result corresponding to the decision model is determined; determining a correction weight corresponding to the reference model based on the first matching result; inputting the second reference value and the reference value sequence into a reference model; and correcting the decision nodes corresponding to the second reference value and the reference value sequence through the correction weight to determine a second matching result.
The second matching result of the reference model is influenced by the first matching result of the output of the decision model, so that the output reference value is matched based on multiple models, and the first matching result and the second matching result have stronger relevance and higher confidence coefficient because the multiple models have the same first matching result.
Step S320, determining the target maintenance personnel according to the first matching result, the first trust weight corresponding to the decision model, the second matching result and the second trust weight corresponding to the reference model.
In this embodiment, each reference model corresponds to a second trust weight. And carrying out weighted calculation according to the first trust weight corresponding to the first matching result and the second trust weight corresponding to each second matching result, and determining the matching rate of each maintainer corresponding to the after-sales work order, so that the maintainer with the highest matching rate is used as the target maintainer.
As an alternative embodiment, referring to fig. 3, step S320 includes:
step S321, obtaining a third trust weight corresponding to each reference model.
In this embodiment, because of at least one reference model, according to the third trust weight and the second reference value corresponding to each reference model, the reference matching rate of the total body corresponding to the reference model is calculated, and at this time, the weight value corresponding to the reference matching rate is the second trust weight. And determining the target maintenance personnel according to the first trust weight, the first reference value, the reference matching rate and the second trust weight.
Step S322, determining a reference matching sequence corresponding to the maintainer based on the third trust weight and the reference matching rate corresponding to each maintainer in the second matching result.
In this embodiment, the reference matching rate is the matching rate corresponding to each serviceman in the second matching result, and a higher matching rate indicates that the serviceman meets the after-sales requirement corresponding to the after-sales work order.
And carrying out weighted calculation based on the third trust weight and the reference matching rate corresponding to each reference model, and determining a reference matching sequence of the reference model overall, wherein the reference matching sequence comprises the calibration matching rate corresponding to each maintenance personnel.
Step S323, determining a maintenance person to be selected based on the coincidence list of the decision matching sequence corresponding to the maintenance person and the reference matching sequence in the first matching result.
In this embodiment, the decision matching sequence is a sequence composed of decision matching rates corresponding to each maintainer in the first matching result.
In order to improve processing efficiency and matching accuracy, extracting maintenance personnel with decision matching rate within a preset ranking from a decision matching sequence as a first list; extracting maintenance personnel in a preset ranking from the reference matching sequence as a second list; and then, based on the maintenance personnel overlapped in the first list and the second list, the maintenance personnel are used as maintenance personnel to be selected.
Step S324, determining the target maintainer based on the decision matching rate of the candidate maintainer, the reference matching sequence, the first trust weight and the second trust weight.
In this embodiment, the decision matching rate of the maintainer to be selected is determined, the calibrated matching rate in the reference matching sequence is obtained, the first trust weight corresponding to the decision model is obtained, the second trust weight of the whole reference model is obtained, weighting calculation is performed based on the decision matching rate, the calibrated matching rate, the first trust weight and the second trust weight, the weighted matching rate corresponding to the maintainer to be selected is determined, and then the maintainer to be selected with the highest weighted matching rate value is used as the target maintainer. The first trust weight and the second trust weight are preset values.
In this embodiment, the to-be-selected maintenance personnel, the decision matching rate and the calibration matching rate determined in the previous steps are combined, so that the weighted matching rate of the to-be-selected maintenance personnel is calculated, the fusion judgment of the decision model and the reference model is realized, namely, the influence of a single model on the decision result is reduced, and the accuracy of the selection of the target maintenance personnel is improved.
In this embodiment, a decision model is generated based on historical after-market information and the impact weight of the fault type on the maintenance results. When determining the target maintenance personnel corresponding to the after-sales work order, inputting the first reference value, the second reference value and the reference value sequence into the decision model and at least one reference model, and determining the target maintenance personnel by using the first estimation result of the decision model and the second estimation result of the reference model.
The present application provides an after-sales service management apparatus, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the after-market service management method of the first embodiment.
Referring now to FIG. 4, a schematic diagram of an after-market service management device suitable for use in implementing embodiments of the present application is shown. The after-sales service management apparatus in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Description: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The after-market service management device shown in fig. 4 is merely an example, and should not impose any limitation on the functionality and scope of use of embodiments of the present application.
As shown in fig. 4, the after-sales service management apparatus may include a processing device 1001 (e.g., a central processor, a graphic processor, etc.), which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the after-sales service management apparatus are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, the following systems may be connected to the I/O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, and the like; an output device 1008 including, for example, a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, and the like; storage device 1003 including, for example, a magnetic tape, a hard disk, and the like; and communication means 1009. The communication means 1009 may allow the after-market service management device to communicate wirelessly or by wire with other devices to exchange data. Although an after-market service management device is shown having various systems, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The after-sales service management equipment provided by the application can solve the technical problem of improving the after-sales service quality of small micro enterprises by adopting the after-sales service management method in the embodiment. Compared with the prior art, the after-sales service management device provided by the application has the same beneficial effects as the after-sales service management device provided by the embodiment, and other technical features in the after-sales service management device are the same as those disclosed by the method of the previous embodiment, and are not repeated herein.
It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for performing the after-market service management method of the above-described embodiments.
The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM: read Only Memory), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wire, fiber optic cable, RF (Radio Frequency), and the like, or any suitable combination of the foregoing.
The above-described computer-readable storage medium may be contained in an after-sales service management apparatus; or may exist alone without being assembled into the after-market service management device.
The computer-readable storage medium carries one or more programs that, when executed by the after-market service management device, cause the after-market service management device to: when an after-sales work order is received, extracting after-sales information contained in the after-sales work order based on character recognition and a work order template corresponding to the after-sales work order; determining a first reference value based on a maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer; performing weighted calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, and determining a second reference value; generating a reference value sequence based on historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address; and inputting the first reference value, the second reference value and the reference value sequence into a decision model and at least one reference model, and determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (i.e. computer program) for executing the after-sales service management method, so that the technical problem of improving the after-sales service quality of small micro enterprises can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the after-sales service management method provided by the above embodiment, and are not described in detail herein.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein, or any application, directly or indirectly, within the scope of the application.

Claims (7)

1. An after-sales service management method is characterized by being applied to an after-sales service management system, wherein the after-sales service management system stores manufacturer information corresponding to a pre-accessed manufacturer and a historical after-sales work order, and the after-sales service management method comprises the following steps:
When an after-sales work order is received, extracting after-sales information contained in the after-sales work order based on character recognition and a work order template corresponding to the after-sales work order;
Determining a first reference value based on a maintenance personnel evaluation corresponding to the manufacturer information in the after-sales information, wherein the maintenance personnel evaluation is evaluation information of the maintenance personnel on the historical after-sales work order of the manufacturer;
Performing weighted calculation based on the equipment type, the fault type and the after-sale address in the after-sale information, and determining a second reference value;
generating a reference value sequence based on historical maintenance data corresponding to the maintenance personnel corresponding to the after-sales address;
Acquiring historical after-sales information, wherein the historical after-sales information comprises the fault type and a maintenance result;
Analyzing an influence factor of the fault type on the maintenance result for each equipment type;
According to the influence factors, integrating all the historical after-sales information, and determining the influence weight of the fault type on the maintenance result;
generating a decision model based on the impact weight;
inputting the first reference value, the second reference value and the reference value sequence into the decision model and at least one reference model, and determining a target maintenance person corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model;
The step of inputting the first reference value, the second reference value and the reference value sequence to the decision model and at least one reference model, and determining a target maintainer corresponding to the after-sales work order based on a first matching result corresponding to the decision model and a second matching result corresponding to the reference model, includes:
Inputting the first reference value, the second reference value and the reference value sequence into the decision model and at least one reference model, and determining the first matching result corresponding to the decision model and the second matching result corresponding to the reference model;
acquiring a third trust weight corresponding to each reference model;
Determining a reference matching sequence corresponding to the maintenance personnel based on the third trust weight and the reference matching rate corresponding to each maintenance personnel in the second matching result;
determining maintenance personnel to be selected based on a coincidence list of the decision matching sequence corresponding to the maintenance personnel and the reference matching sequence in the first matching result;
And determining the target maintenance personnel based on the decision matching rate of the maintenance personnel to be selected, the reference matching sequence, the first trust weight corresponding to the decision model and the second trust weight corresponding to the reference model.
2. The method of claim 1, wherein the step of extracting after-market information contained in the after-market worksheet based on text recognition and a worksheet template corresponding to the after-market worksheet when the after-market worksheet is received comprises:
Determining the work order template based on the work order identification of the after-sales work order;
acquiring coordinate information of each content unit in the work order template on target paper;
partitioning the after-market worksheet into the content units based on the coordinate information;
and extracting the content unit based on the character recognition, and determining the after-sales information.
3. The method of claim 1, wherein the step of determining the first reference value based on a serviceman evaluation corresponding to the manufacturer information in the after-market information comprises:
acquiring the historical after-sales work order of the fault type in the after-sales information corresponding to the manufacturer information;
acquiring the evaluation information corresponding to each maintainer in the historical after-sales work order;
And determining the first reference value based on a first mapping score corresponding to the evaluation information and the rating of the maintenance personnel.
4. The method of claim 3, wherein the step of determining the first reference value based on the first mapping score corresponding to the evaluation information and the maintenance person's rating comprises:
Determining the first mapping score based on the evaluation score in the evaluation information and the semantic recognition result corresponding to the evaluation information;
Taking the rating of the maintenance personnel as the weight of the rating information corresponding to the maintenance personnel;
and carrying out weighted calculation based on the weight of the evaluation information and the first mapping score to determine the first reference value.
5. The method of claim 1, wherein the step of determining the second reference value based on the weighted calculation of the device type, the fault type, and the after-market address in the after-market information comprises:
determining the equipment type and determining first maintenance difficulty;
determining a second repair difficulty based on the first repair difficulty and the fault type;
determining third maintenance difficulty based on the area level corresponding to the after-sales address;
Determining a weight sequence based on the manufacturer information;
and carrying out weighted calculation according to the weight sequence, the first maintenance difficulty, the second maintenance difficulty and the third maintenance difficulty, and determining the second reference value.
6. An after-sales service management apparatus characterized in that, the after-sales service management apparatus includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program configured to implement the steps of the after-market service management method of any one of claims 1 to 5.
7. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the after-sales service management method according to any one of claims 1 to 5.
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CN117035246A (en) * 2022-04-29 2023-11-10 中国移动通信集团广东有限公司 Scheduling method, device and equipment of maintenance work order and readable storage medium

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