Automatic docking management method and system for bill installment business

Abstract

The present application discloses an automatic docking management method and system for bill installment business, which relates to the field of data processing technology. The method includes: loading a first pending installment bill information set and a docking personnel database; performing data authentication to generate a second pending installment bill information set; establishing a multi-level bill user installment trust constraint, performing a bill installment trust review, and generating a third pending installment bill information set; performing a processing sequence constraint to generate a fourth pending installment bill information set; performing feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators, generating multiple personnel feature information sources, performing task allocation based on multiple personnel feature information sources and the fourth pending installment bill information set based on a bill docking scheduling algorithm, generating a docking business allocation result, and executing the installment bill docking management of the fourth pending installment bill information set. Thus, the technical effect of improving docking adaptability and optimizing management efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an automatic docking management method and system for bill installment services. Background Art

[0002] In financial services, bill installment business is a common consumer credit method that helps consumers pay for goods or services in installments. Traditional bill installment business management methods usually require consumers to provide a large amount of personal and financial information, and use a combination of manual and simple rules to allocate and process bill installment business. This has technical problems such as poor docking effect and low management efficiency. Summary of the invention

[0003] The purpose of this application is to provide an automatic docking management method and system for bill installment services, so as to solve the technical problems of poor docking effect and low management efficiency in the prior art.

[0004] In view of the above technical problems, the present application provides an automatic docking management method and system for bill installment business.

[0005] In a first aspect, the present application provides an automatic docking management method for a bill installment service, wherein the method comprises:

[0006] According to the bill installment business management module, a first pending installment bill information set and a docking personnel database are loaded, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills;

[0007] Performing data authentication on the first to-be-processed installment bill information set based on bill information quality constraints to generate a second to-be-processed installment bill information set;

[0008] Establishing a multi-level bill user installment trust constraint based on the second installment bill information set to be processed, and performing a bill installment trust review on the second installment bill information set to be processed based on the multi-level bill user installment trust constraint to generate a third installment bill information set to be processed;

[0009] Performing processing sequence constraints based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set;

[0010] Performing feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators;

[0011] Based on the bill docking scheduling algorithm, task allocation is performed according to the multiple personnel feature information sources and the fourth set of installment bill information to be processed, a docking business allocation result is generated, and the installment bill docking management of the fourth set of installment bill information to be processed is executed according to the docking business allocation result.

[0012] In a second aspect, the present application also provides an automatic docking management system for bill installment services, wherein the system includes:

[0013] A task acquisition unit, the task acquisition unit is used to load a first pending installment bill information set and a docking personnel database according to the bill installment business management module, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills;

[0014] A data authentication unit, the data authentication unit is used to perform data authentication on the first to-be-processed installment bill information set based on the bill information quality constraint to generate a second to-be-processed installment bill information set;

[0015] A trust review unit, the trust review unit being used to establish a multi-level bill user installment trust constraint based on the second installment bill information set to be processed, and to perform a bill installment trust review on the second installment bill information set to be processed based on the multi-level bill user installment trust constraint to generate a third installment bill information set to be processed;

[0016] A sequence processing unit, the sequence processing unit is used to process the sequence constraint based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set;

[0017] A personnel feature acquisition unit, the personnel feature acquisition unit is used to perform feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators;

[0018] A bill docking scheduling unit, which is used to perform task allocation based on a bill docking scheduling algorithm, according to the multiple personnel feature information sources and the fourth set of installment bill information to be processed, generate a docking business allocation result, and execute the installment bill docking management of the fourth set of installment bill information to be processed according to the docking business allocation result.

[0019] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0020] Through the bill installment business management module, a first pending installment bill information set and a docking personnel database are loaded, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills; based on the bill information quality constraint, data authentication is performed on the first pending installment bill information set to generate a second pending installment bill information set; based on the second pending installment bill information set, a multi-level bill user installment trust constraint is established, and based on the multi-level bill user installment trust constraint, a bill installment trust review is performed on the second pending installment bill information set to generate a third pending installment bill information set; based on the third pending installment bill information set, a multi-level bill user installment trust constraint is established, and a bill installment trust review is performed on the second pending installment bill information set to generate a third pending installment bill information set; based on the third pending installment bill information set, a multi-level bill user installment trust constraint is established, and a multi-level bill user installment trust constraint is established. The fourth instalment bill information set to be processed is processed in sequence constraints to generate the fourth instalment bill information set to be processed; the docking personnel database is feature identified according to the multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators; based on the bill docking scheduling algorithm, tasks are allocated according to multiple personnel feature information sources and the fourth instalment bill information set to be processed, docking business allocation results are generated, and the instalment bill docking management of the fourth instalment bill information set to be processed is executed according to the docking business allocation results. Thus, the technical effect of improving docking adaptability and optimizing management efficiency is achieved.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly illustrate the technical means of the present application and to implement it in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The embodiments of the present invention and the following brief description are illustrated in conjunction with the accompanying drawings, which are described as follows:

[0023] Figure 1 This is a flowchart of the automatic docking management method for bill installment business used in this application;

[0024] Figure 2 This is a structural diagram of the automatic docking management system used for bill installment business in this application.

[0025] Explanation of the reference numerals: task acquisition unit 11, data authentication unit 12, trust review unit 13, sequence processing unit 14, personnel feature acquisition unit 15, bill docking scheduling unit 16. DETAILED DESCRIPTION

[0026] The present application solves the technical problems of poor docking effect and low management efficiency faced by the prior art by providing an automatic docking management method and system for bill installment business.

[0027] The solution in the embodiment of the present technology, in order to solve the above problems, adopts the following overall ideas:

[0028] First, through the bill installment business management module, load the first pending installment bill information set and the docking personnel database, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills. Next, based on the bill information quality constraint, the first pending installment bill information set is data authenticated to generate a second pending installment bill information set. Then, based on the second pending installment bill information set, a multi-level bill user installment trust constraint is established, and the second pending installment bill information set is subject to a bill installment trust review based on the multi-level bill user installment trust constraint to generate a third pending installment bill information set. Next, based on the third pending installment bill information set, a processing sequence constraint is performed to generate a fourth pending installment bill information set. Subsequently, feature identification is performed on the docking personnel database based on multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators, and personnel docking service evaluation indicators. Finally, based on the bill docking scheduling algorithm, tasks are assigned according to multiple personnel feature information sources and the fourth pending installment bill information set, docking business assignment results are generated, and the installment bill docking management of the fourth pending installment bill information set is executed according to the docking business assignment results. Thus, the technical effect of improving docking adaptability and optimizing management efficiency is achieved.

[0029] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0030] Embodiment 1

[0031] Figure 1 The present invention is a flowchart of an automatic docking management method for bill installment services; wherein, it includes:

[0032] S100: According to the bill installment business management module, a first to-be-processed installment bill information set and a docking personnel database are loaded, wherein the first to-be-processed installment bill information set includes multiple installment bill information corresponding to multiple to-be-processed installment bills;

[0033] Optionally, a first batch of pending installment bill information sets is obtained from a data source, including multiple installment bill information and feature information corresponding to multiple pending installment bills. Exemplarily, first, a database containing docking personnel information is loaded for subsequent bill processing and management. Dock personnel may include customer service personnel, financial personnel, etc. Then, the first pending installment bill information set is associated and integrated with the docking personnel database for subsequent bill processing and management.

[0034] Optionally, the bill installment business management module uses a database query language or data processing tool to obtain and load the first pending installment bill information set and the docking personnel database. The first pending installment bill information set and the docking personnel database obtained through the above steps provide data support for subsequent business processing.

[0035] S200: Performing data authentication on the first to-be-processed installment bill information set based on bill information quality constraints to generate a second to-be-processed installment bill information set;

[0036] Optionally, the data of the first pending instalment bill information set is authenticated through bill information quality constraints, the purpose of which is to judge the information quality of the first pending instalment bill information obtained, and to provide feedback and corrections to the information that does not meet the quality requirements, so as to generate a second pending instalment bill information set that meets the quality requirements, and provide a reliable data basis for subsequent bill processing and management. Among them, the bill information quality constraints of data authentication include data integrity, accuracy, consistency, timeliness, etc.

[0037] S300: establishing a multi-level bill user installment trust constraint based on the second installment bill information set to be processed, and performing a bill installment trust review on the second installment bill information set to be processed based on the multi-level bill user installment trust constraint to generate a third installment bill information set to be processed;

[0038] Optionally, a multi-level bill user installment trust constraint is established based on the second to-be-processed installment bill information set, wherein the multi-level bill user installment trust constraint includes multiple trust levels and their corresponding installment bill data constraints. In other words, the multi-level bill user installment trust constraint specifies the mapping relationship between user levels and installment bill restrictions.

[0039] Exemplarily, different bill user installment trust constraints are established for different installment numbers and installment repayment amounts. First, determine the range and conditions of different installment numbers and installment repayment amounts. For example, you can consider installments of 3, 6, 9, and 12 months, and installment repayment amounts of 1000, 2000, 3000 and other different amount levels. Then, for each combination of installment number and installment repayment amount, determine the corresponding trust constraints. These constraints may include user credit rating requirements: set user credit rating requirements according to the number of installments and repayment amounts; income status requirements: require users to have corresponding income status according to the number of installments and repayment amounts; historical repayment record requirements: require users to have a good repayment record in the past, no overdue situations, etc.

[0040] Optionally, the installment information of each bill user is reviewed based on the number of installments and the installment repayment amount, and the corresponding trust constraints are applied. The review results include passed, further review required, or failed. By establishing trust constraints for different installment numbers and installment repayment amounts, trust review can be performed on bills based on the user's installment situation and repayment ability, improving the accuracy and efficiency of bill processing.

[0041] S400: Performing processing sequence constraints based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set;

[0042] Optionally, based on the characteristic data of the installment users and the installment bills in the third installment bill information set to be processed, the third installment bill information set to be processed is subjected to a processing sequence constraint to obtain a fourth installment bill information set to be processed. Installment users with higher user levels have higher processing priorities and correspondingly have higher rankings in the sequential processing. The fourth installment bill information set to be processed is a serialized installment bill information table column.

[0043] Optionally, the processing sequence constraints are based on multivariate feature data, including bill due date, bill application time, bill risk, user level, user performance, user repayment ability, etc.

[0044] S500: performing feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators;

[0045] Optionally, the docking personnel refers to the staff who handles and manages the installment billing business, who is responsible for processing the user's installment application, generating a repayment plan, tracking the repayment status, etc. The docking personnel include customer service personnel, financial personnel, installment business management personnel, etc. The characteristic information of the docking personnel may include basic attributes, work experience, skill level, business familiarity, service evaluation and other multi-dimensional indicators, which are used to evaluate and manage them. By analyzing and identifying the characteristic information of the docking personnel, the docking personnel can be better equipped and managed to improve the efficiency and quality of the installment billing business. Among them, the docking personnel database is a structured database embedded in the installment business providing platform or providing the main system.

[0046] Exemplarily, first, determine the multidimensional indicators used to identify personnel characteristics, including personnel basic attribute indicators (such as age, gender, education, etc.), personnel docking skill experience indicators (such as professional skills, work experience, etc.), personnel fixed assignment business indicators (such as job responsibilities, assigned tasks, etc.) and personnel docking service evaluation indicators (such as customer satisfaction, work performance evaluation, etc.). Then, according to the determined multidimensional personnel characteristic indicators, each docking personnel is identified based on the docking personnel database, and the corresponding characteristic information is extracted. Specifically, data mining or machine learning technology is used to perform feature identification, and the personnel characteristics in the database are converted into a data source that can be used for analysis and evaluation. Next, the characteristic information corresponding to the multiple dimensional indicators of each person is combined into multiple personnel characteristic information sources, and the multiple personnel characteristic information sources reflect the business processing capabilities of multiple docking personnel.

[0047] S600: Based on the bill docking scheduling algorithm, task allocation is performed according to the multiple personnel feature information sources and the fourth set of installment bill information to be processed, a docking business allocation result is generated, and installment bill docking management of the fourth set of installment bill information to be processed is executed according to the docking business allocation result.

[0048] Optionally, the bill docking scheduling algorithm is determined based on the target scenario requirements and is configured with a scheduling strategy that responds to the target scenario requirements to perform task allocation based on multiple personnel characteristic information sources and a fourth set of pending installment bill information, thereby ensuring that pending installment bills can be efficiently and reasonably allocated to multiple docking personnel for processing.

[0049] Optionally, according to the determined task allocation algorithm, the instalment bill task in the fourth pending instalment bill information set is allocated to the docking personnel. The allocation process is designed based on factors such as the workload, skill matching, and historical performance of the docking personnel.

[0050] Optionally, based on the task allocation results, a docking business allocation result is generated, including the matching of installment bills and docking personnel, the processing sequence of installment bills, etc.

[0051] Optionally, according to the docking business allocation results, docking management of installment bills is performed. This includes supervising the work progress of docking personnel, tracking the processing results of installment bills, and promptly handling abnormal situations, so as to improve the efficiency and quality of docking business.

[0052] Furthermore, in some embodiments, data authentication is performed on the first to-be-processed installment bill information set based on the bill information quality constraint to generate a second to-be-processed installment bill information set, including:

[0053] Performing data quality evaluation on the multiple installment bill information respectively to obtain multiple bill information quality evaluation coefficients;

[0054] respectively judging whether the plurality of bill information quality evaluation coefficients satisfy the bill information quality constraint, and obtaining a plurality of bill information quality judgment results;

[0055] Classifying the multiple installment bill information based on the multiple bill information quality judgment results to generate a normal installment bill information set and an abnormal installment bill information set;

[0056] Sending the abnormal installment bill information set to the user interaction interface to obtain an abnormal bill user feedback result set;

[0057] The abnormal installment bill information set is adjusted based on the abnormal bill user feedback result set to generate an adjusted installment bill information set, and the normal installment bill information set and the adjusted installment bill information set are added to the second installment bill information set to be processed.

[0058] Optionally, data quality evaluation is performed on multiple installment bill information to obtain multiple bill information quality evaluation coefficients, wherein the bill information quality evaluation coefficients quantitatively reflect the data quality of the installment bill information.

[0059] Exemplarily, data quality evaluation involves data integrity evaluation coefficients: reflecting the completeness of data in the installment bill information, including whether important fields or information are missing; data accuracy evaluation coefficients: reflecting the accuracy of data in the installment bill information, including whether there are erroneous or inconsistent data; data consistency evaluation coefficients: reflecting the consistency of data in the installment bill information, including the consistency of data from different data sources or at different time points; data timeliness evaluation coefficients: reflecting the update frequency and timeliness of the installment bill information, including whether the latest bill information is reflected in a timely manner; data availability evaluation coefficients: reflecting the availability of the installment bill information, including whether the data is easy to obtain and use. These evaluation coefficients can objectively reflect the data quality of the installment bill information.

[0060] Optionally, the bill information quality evaluation coefficient is a weighted sum coefficient of the above-mentioned data integrity evaluation coefficient, data accuracy evaluation coefficient, data consistency evaluation coefficient, data timeliness evaluation coefficient, and data availability evaluation coefficient.

[0061] Optionally, it is determined whether multiple bill information quality evaluation coefficients meet the bill information quality constraint, and multiple bill information quality judgment results are obtained. Based on the bill information quality judgment results, multiple installment bill information are classified to generate a normal installment bill information set and an abnormal installment bill information set. Among them, the installment bill information whose bill information quality evaluation coefficient meets the bill information quality constraint is stored in the normal installment bill information set, and the installment bill information whose bill information quality evaluation coefficient does not meet the bill information quality constraint is stored in the abnormal installment bill information set.

[0062] Optionally, the abnormal installment bill information set is sent to the user interaction interface, and the abnormal bill information is displayed on the user interaction interface for the user to view and process. After viewing the abnormal bill information, the user can provide feedback. The feedback may include confirmation of the bill information, additional information, or objections. The user interaction interface collects the user's feedback information and forms an abnormal bill user feedback result set.

[0063] Optionally, according to the user's feedback information, the abnormal bill information is processed accordingly, including the need to adjust the bill information, explain the bill content, etc., so as to process and manage the abnormal bill information and improve the efficiency and accuracy of bill processing.

[0064] Optionally, the normal installment bill information set and the adjusted installment bill information set are added to the second installment bill information set to be processed as basic data for the next step of processing. Through the above steps, the installment bill information can be comprehensively processed and managed to ensure the smooth progress of the installment bill business.

[0065] Furthermore, in some embodiments, based on the multi-level bill user installment trust constraint, the second to-be-processed installment bill information set is subjected to a bill installment trust review to generate a third to-be-processed installment bill information set, including:

[0066] Collecting the bill installment repayment record data of each user corresponding to the second to-be-processed installment bill information set, obtaining multiple user bill installment repayment record sets, and performing user bill installment repayment credit evaluation based on the multiple user bill installment repayment record sets to generate multiple user bill installment repayment credit coefficients;

[0067] Collecting financial information of each user corresponding to the second to-be-processed installment bill information set to obtain multiple user financial information sets, and evaluating the user financial status based on the multiple user financial information sets to generate multiple user financial status coefficients;

[0068] Based on the first weight distribution constraint condition, weighted calculations are performed on the multiple user bill installment repayment credit coefficients and the multiple user financial status coefficients to generate multiple user installment trust coefficients;

[0069] Based on the multi-level bill user installment trust constraint, installment trusted user authentication is performed according to the multiple user installment trust coefficients to generate multiple installment trusted users;

[0070] The installment bill information of the second to-be-processed installment bill information set is matched based on the multiple installment trusted users to obtain the third to-be-processed installment bill information set.

[0071] Optionally, a user bill installment repayment credit evaluation is performed based on the user bill installment repayment record set to obtain the user bill installment repayment credit coefficient. The user bill installment repayment credit coefficient reflects the user's past installment bill performance. A higher user bill installment repayment credit coefficient corresponds to better performance behavior, including, for example, the frequency and frequency of scheduled repayment, overdue repayment, and early repayment.

[0072] Optionally, the user's financial status is evaluated based on the user's financial information set to obtain the user's financial status coefficient, wherein the user's financial information set includes financial information such as income, expenditure, assets, and liabilities. The user's financial status is evaluated based on the user's financial information, which may include evaluations of the balance of income and expenditure, liabilities, and asset-liability ratio. By combining the evaluation results of the above multiple aspects, a financial status coefficient is generated for each user, and the financial status coefficient reflects the degree of good or bad of his or her financial status. Exemplarily, the evaluation results of the above multiple aspects are combined and weighted aggregation of the evaluation results of multiple aspects is performed based on the tendency of risk control requirements.

[0073] Optionally, based on the multi-level bill user installment trust constraint, installment trusted user authentication is performed according to multiple user installment trust coefficients to generate multiple installment trusted users. The authentication method can be judged according to the constraint conditions to ensure that users who meet the trust constraints are authenticated as installment trusted users. Then, based on multiple installment trusted users, the installment bill information is matched in the second to-be-processed installment bill information set to obtain a third to-be-processed installment bill information set. The installment bill information in the third to-be-processed installment bill information set are all installment bills of installment trusted users.

[0074] Through the above steps, the user installment trust coefficient is generated according to the user's credit and financial status, and the installment trust user is authenticated based on these coefficients, and then the installment bill information is matched to obtain the installment bill that meets the trust constraints, thereby eliminating invalid installment bills and improving the efficiency and accuracy of installment bill processing.

[0075] Furthermore, in some embodiments, a fourth installment bill information set to be processed is generated based on the processing sequence constraint of the third installment bill information set to be processed, including:

[0076] Extracting multiple installment bill time zone information according to the third to-be-processed installment bill information set, and performing time sequence priority evaluation based on the multiple installment bill time zone information to generate multiple bill time sequence priority coefficients;

[0077] Obtaining multiple user attribute information corresponding to the third to-be-processed installment bill information set, and performing user priority evaluation based on the multiple user attribute information to generate multiple bill user priority coefficients;

[0078] Based on the second weight distribution constraint condition, weighted calculation is performed on the multiple billing time sequence priority coefficients and the multiple billing user priority coefficients respectively to generate multiple billing priority processing coefficients;

[0079] The third to-be-processed installment bill information set is arranged in descending order based on the multiple bill priority processing coefficients to obtain the fourth to-be-processed installment bill information set.

[0080] Among them, the installment bill time zone information refers to the submission time information of the installment bill. The earlier the installment bill is submitted, the better the bill timing priority coefficient will be. User attribute information refers to the user level information of the installment user. Users of different levels have different priorities, corresponding to multiple bill user priority coefficients, which affect their priority in the installment bill processing.

[0081] Optionally, according to the second weight distribution constraint, multiple billing time sequence priority coefficients and multiple billing user priority coefficients are weighted to generate multiple billing priority processing coefficients. Then, the third pending installment bill information set is sorted in descending order according to the multiple billing priority processing coefficients to obtain a fourth pending installment bill information set.

[0082] Furthermore, in some embodiments, based on the bill docking scheduling algorithm, task allocation is performed according to the multiple personnel feature information sources and the fourth to-be-processed installment bill information set to generate a docking business allocation result, including:

[0083] Extracting multiple personnel docking feature data groups corresponding to multiple docking personnel according to the multiple personnel feature information sources, wherein each personnel docking feature data group includes personnel basic attribute data, personnel docking skill experience data and personnel docking service evaluation data corresponding to each docking personnel;

[0084] Based on the bill connection feature analysis model, task allocation is performed on each installment bill information in the fourth installment bill information set to be processed according to the multiple personnel connection feature data groups, and multiple bill connection business allocation results are generated;

[0085] Extracting multiple fixed personnel allocation business information corresponding to the multiple docking personnel according to the multiple personnel feature information sources, and updating the multiple fixed personnel allocation business information according to the multiple bill docking business allocation results to generate multiple personnel update allocation business information;

[0086] Based on the updated allocation business information of the multiple personnel, the multiple bill docking business allocation results are balanced and optimized to obtain the docking business allocation result.

[0087] Among them, the basic attribute data of each docking person is extracted from the multi-dimensional personnel characteristic indicators, including name, age, gender, work experience, etc.; the docking skill experience data of each docking person includes professional skills, work experience, training experience, etc. The docking service evaluation data of each docking person includes user evaluation, work performance evaluation, etc.

[0088] Optionally, the extracted personnel basic attribute data, personnel matching skill experience data and personnel matching service evaluation data are combined into a personnel matching feature data group, representing the complete feature information of each matching personnel.

[0089] Optionally, the fixed allocation business information refers to a serialized form of pending bill docking business assigned to each docking personnel. The fixed allocation business information is updated based on multiple bill docking business allocation results, thereby obtaining new allocation business information, which is stored as updated allocation business information.

[0090] Further, in some implementations, based on the bill docking feature parsing model, tasks are assigned to each installment bill information in the fourth installment bill information set to be processed according to the multiple personnel docking feature data groups, and multiple bill docking business assignment results are generated, including:

[0091] Extracting first installment bill information according to the fourth to-be-processed installment bill information set;

[0092] The bill docking feature analysis model includes an input layer, a docking personnel demand feature analysis layer, a docking feature matching analysis layer and an output layer;

[0093] Inputting the first installment bill information into the docking personnel demand feature analysis layer to obtain first bill docking personnel demand feature data;

[0094] Based on the docking feature matching analysis layer, matching analysis is performed on the first bill docking personnel demand feature data and the multiple personnel docking feature data groups respectively to obtain multiple docking feature matching coefficients;

[0095] Based on the multiple docking feature matching coefficients, selecting the optimal docking feature matching coefficient, and locating the multiple docking personnel based on the optimal docking feature matching coefficient to obtain a first bill docking service allocation result;

[0096] By analogy, based on the bill docking feature analysis model, the fourth pending installment bill information set continues to be assigned tasks according to the multiple personnel docking feature data groups, and combined with the first bill docking business assignment result, the multiple bill docking business assignment results are obtained.

[0097] Optionally, the input layer of the bill docking feature parsing model is used to serially receive the installment bill information from the fourth installment bill information set to be processed, the docking personnel demand feature analysis layer is used to parse and generate the docking personnel demand feature data of the bill based on the installment bill information, the docking feature matching analysis layer is used to perform the optimal matching of the docking personnel demand feature data with multiple personnel docking feature data groups, and the output layer is used to generate and output the bill docking business allocation results based on the above-mentioned optimal matching results, thereby obtaining multiple bill docking business allocation results.

[0098] Further, in some implementations, the multiple bill docking service allocation results are balanced and optimized based on the multiple personnel update allocation service information to obtain the docking service allocation result, including:

[0099] Divide the multiple personnel update allocation service information into time zones based on preset time zone constraints to generate multiple time zone personnel allocation service groups;

[0100] Based on the multiple time zone personnel allocation business groups, a time zone business allocation balance calculation is performed to obtain multiple time zone business allocation balance coefficients;

[0101] When any one of the multiple time zone business allocation balancing coefficients does not satisfy the time zone business allocation balancing constraint, an abnormal time zone personnel allocation business result is generated;

[0102] Performing a balanced adjustment based on the abnormal time zone personnel allocation business result to generate an adjusted personnel allocation business result;

[0103] The plurality of bill docking business allocation results are optimized based on the adjustment personnel allocation business result to generate the docking business allocation result.

[0104] The preset time zone constraint refers to a balanced granularity range of task distribution, which can be divided into hours, days, weeks, etc. for example.

[0105] Optionally, by calculating the standard deviation of the allocated business volume of multiple liaison personnel corresponding to each time zone, a business allocation balance calculation is performed in the same time zone to obtain multiple time zone business allocation balance coefficients. When there are situations in which the time zone business allocation balance constraints are not satisfied in the multiple time zone business allocation balance coefficients, it means that there is a large load difference in the task allocation of multiple time zone personnel allocation business groups in the current time zone, and adjustment is required.

[0106] Through the above steps, balanced adjustments can be made based on the business results of personnel allocation in abnormal time zones, and multiple bill docking business allocation results can be optimized to generate the final docking business allocation results, ensuring that the task processing capabilities of each docking personnel are met to the greatest extent, avoiding local accumulation or processing timeouts due to uneven distribution of bill installment business.

[0107] In summary, the automatic docking management method for bill installment business provided by the present invention has the following technical effects:

[0108] Through the bill installment business management module, a first pending installment bill information set and a docking personnel database are loaded, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills; based on the bill information quality constraint, data authentication is performed on the first pending installment bill information set to generate a second pending installment bill information set; based on the second pending installment bill information set, a multi-level bill user installment trust constraint is established, and based on the multi-level bill user installment trust constraint, a bill installment trust review is performed on the second pending installment bill information set to generate a third pending installment bill information set; based on the third pending installment bill information set, a multi-level bill user installment trust constraint is established, and a bill installment trust review is performed on the second pending installment bill information set to generate a third pending installment bill information set; based on the third pending installment bill information set, a multi-level bill user installment trust constraint is established, and a multi-level bill user installment trust constraint is established. The fourth instalment bill information set to be processed is processed in sequence constraints to generate the fourth instalment bill information set to be processed; the docking personnel database is feature identified according to the multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators; based on the bill docking scheduling algorithm, tasks are allocated according to multiple personnel feature information sources and the fourth instalment bill information set to be processed, docking business allocation results are generated, and the instalment bill docking management of the fourth instalment bill information set to be processed is executed according to the docking business allocation results. Thus, the technical effect of improving docking adaptability and optimizing management efficiency is achieved.

[0109] Embodiment 2

[0110] Based on the same concept as the automatic docking management method for bill installment business in the above embodiment, Figure 2As shown, the present application also provides an automatic docking management system for bill installment business, the system comprising:

[0111] The task acquisition unit 11 is used to load a first pending installment bill information set and a docking personnel database according to the bill installment business management module, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills;

[0112] A data authentication unit 12, configured to perform data authentication on the first to-be-processed installment bill information set based on the bill information quality constraint to generate a second to-be-processed installment bill information set;

[0113] A trust review unit 13 is used to establish a multi-level bill user installment trust constraint based on the second to-be-processed installment bill information set, and perform a bill installment trust review on the second to-be-processed installment bill information set based on the multi-level bill user installment trust constraint to generate a third to-be-processed installment bill information set;

[0114] A sequence processing unit 14 is used to perform a processing sequence constraint based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set;

[0115] A personnel feature acquisition unit 15 is used to perform feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators;

[0116] The bill docking scheduling unit 16 is used to perform task allocation based on the bill docking scheduling algorithm, according to the multiple personnel feature information sources and the fourth set of installment bill information to be processed, generate a docking business allocation result, and execute the installment bill docking management of the fourth set of installment bill information to be processed according to the docking business allocation result.

[0117] Furthermore, the data authentication unit 12 also includes:

[0118] A data quality evaluation unit, used to perform data quality evaluation on the plurality of installment bill information respectively to obtain a plurality of bill information quality evaluation coefficients;

[0119] A quality constraint unit, used to respectively determine whether the multiple bill information quality evaluation coefficients meet the bill information quality constraint, and obtain multiple bill information quality judgment results;

[0120] A classification unit, configured to classify the plurality of instalment bill information based on the plurality of bill information quality judgment results, and generate a normal instalment bill information set and an abnormal instalment bill information set;

[0121] An abnormal feedback unit, used to send the abnormal installment bill information set to the user interaction interface to obtain an abnormal bill user feedback result set;

[0122] An adjustment unit is used to adjust the abnormal installment bill information set based on the abnormal bill user feedback result set, generate an adjusted installment bill information set, and add the normal installment bill information set and the adjusted installment bill information set to the second installment bill information set to be processed.

[0123] Furthermore, the trust audit unit 13 also includes:

[0124] a repayment credit evaluation unit, configured to collect bill installment repayment record data of each user corresponding to the second to-be-processed installment bill information set, obtain multiple user bill installment repayment record sets, and perform user bill installment repayment credit evaluation based on the multiple user bill installment repayment record sets to generate multiple user bill installment repayment credit coefficients;

[0125] a financial status evaluation unit, configured to collect financial information of each user corresponding to the second to-be-processed installment bill information set, obtain multiple user financial information sets, and evaluate the user financial status based on the multiple user financial information sets to generate multiple user financial status coefficients;

[0126] An installment trust summing unit is used to perform weighted calculations on the multiple user bill installment repayment credit coefficients and the multiple user financial status coefficients based on the first weight distribution constraint condition, and generate multiple user installment trust coefficients;

[0127] An installment trusted user authentication unit, configured to perform installment trusted user authentication based on the multi-level bill user installment trust constraint and the multiple user installment trust coefficients to generate multiple installment trusted users;

[0128] The installment bill information matching unit is used to match the installment bill information of the second to-be-processed installment bill information set based on the multiple installment trusted users to obtain the third to-be-processed installment bill information set.

[0129] Furthermore, the sequence processing unit 14 also includes:

[0130] A time sequence priority evaluation unit, configured to extract multiple installment bill time zone information according to the third to-be-processed installment bill information set, and perform time sequence priority evaluation based on the multiple installment bill time zone information to generate multiple bill time sequence priority coefficients;

[0131] A user priority evaluation unit, configured to obtain a plurality of user attribute information corresponding to the third to-be-processed installment bill information set, and perform user priority evaluation based on the plurality of user attribute information to generate a plurality of bill user priority coefficients;

[0132] a priority processing calculation unit, configured to perform weighted calculations on the plurality of billing time sequence priority coefficients and the plurality of billing user priority coefficients respectively based on a second weight distribution constraint condition to generate a plurality of billing priority processing coefficients;

[0133] The bill information arrangement unit is used to arrange the third to-be-processed installment bill information set in descending order based on the multiple bill priority processing coefficients to obtain the fourth to-be-processed installment bill information set.

[0134] Furthermore, the bill connection scheduling unit 16 also includes:

[0135] A personnel characteristic information calling unit, used to extract a plurality of personnel docking characteristic data groups corresponding to a plurality of docking personnel according to the plurality of personnel characteristic information sources, wherein each personnel docking characteristic data group includes personnel basic attribute data, personnel docking skill experience data and personnel docking service evaluation data corresponding to each docking personnel;

[0136] A business allocation unit, configured to allocate tasks to each of the installment bill information in the fourth to-be-processed installment bill information set based on the bill docking feature parsing model and the plurality of personnel docking feature data groups, and generate a plurality of bill docking business allocation results;

[0137] An updating allocation unit, configured to extract a plurality of fixed personnel allocation service information corresponding to the plurality of docking personnel according to the plurality of personnel feature information sources, and update the plurality of fixed personnel allocation service information according to the plurality of bill docking service allocation results to generate a plurality of personnel update allocation service information;

[0138] A balanced optimization unit is used to perform balanced optimization adjustment on the multiple bill docking business allocation results based on the multiple personnel update allocation business information to obtain the docking business allocation result.

[0139] In some implementations, the service allocation unit in the bill connection scheduling unit 16 is further configured to:

[0140] Extracting first installment bill information according to the fourth to-be-processed installment bill information set;

[0141] The bill docking feature analysis model includes an input layer, a docking personnel demand feature analysis layer, a docking feature matching analysis layer and an output layer;

[0142] Inputting the first installment bill information into the docking personnel demand feature analysis layer to obtain first bill docking personnel demand feature data;

[0143] Based on the docking feature matching analysis layer, matching analysis is performed on the first bill docking personnel demand feature data and the multiple personnel docking feature data groups respectively to obtain multiple docking feature matching coefficients;

[0144] Based on the multiple docking feature matching coefficients, selecting the optimal docking feature matching coefficient, and locating the multiple docking personnel based on the optimal docking feature matching coefficient to obtain a first bill docking service allocation result;

[0145] By analogy, based on the bill docking feature analysis model, the fourth pending installment bill information set continues to be assigned tasks according to the multiple personnel docking feature data groups, and combined with the first bill docking business assignment result, the multiple bill docking business assignment results are obtained.

[0146] In some implementations, the balancing optimization unit in the bill connection scheduling unit 16 further includes:

[0147] A time zone division unit, configured to divide the multiple personnel update allocation service information into time zones based on preset time zone constraints, and generate multiple time zone personnel allocation service groups;

[0148] A balance calculation unit, configured to perform a balance calculation of business allocation in the same time zone based on the multiple time zone personnel allocation business groups, and obtain a balance coefficient of business allocation in multiple time zones;

[0149] A balancing constraint unit, configured to generate an abnormal time zone personnel allocation business result when any one of the plurality of time zone business allocation balancing coefficients does not satisfy the time zone business allocation balancing constraint;

[0150] A balancing adjustment unit, configured to perform balancing adjustment based on the abnormal time zone personnel allocation business result and generate an adjusted personnel allocation business result;

[0151] The docking business allocation result optimization unit is used to optimize the multiple bill docking business allocation results based on the adjustment personnel allocation business result to generate the docking business allocation result.

[0152] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the automatic docking management system for bill installment business described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0153] It should be understood that the embodiments disclosed in this application and the above description can enable those skilled in the art to use this application to implement this application. At the same time, this application is not limited to the above-mentioned embodiments, and obvious modifications, combinations and substitutions of the embodiments mentioned in this application also fall within the scope of protection of this application.

Claims

1. An automatic docking management method for bill installment business, characterized in that: The method comprises: According to the bill installment business management module, a first pending installment bill information set and a docking personnel database are loaded, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills; Performing data authentication on the first to-be-processed installment bill information set based on bill information quality constraints to generate a second to-be-processed installment bill information set; Establishing a multi-level bill user installment trust constraint based on the second installment bill information set to be processed, and performing a bill installment trust review on the second installment bill information set to be processed based on the multi-level bill user installment trust constraint to generate a third installment bill information set to be processed; Performing processing sequence constraints based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set; Performing feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators; Based on the bill docking scheduling algorithm, tasks are assigned according to the multiple personnel feature information sources and the fourth to-be-processed installment bill information set, a docking business assignment result is generated, and installment bill docking management of the fourth to-be-processed installment bill information set is executed according to the docking business assignment result; Wherein, based on the bill docking scheduling algorithm, task allocation is performed according to the multiple personnel feature information sources and the fourth to-be-processed installment bill information set to generate a docking business allocation result, including: Extracting multiple personnel docking feature data groups corresponding to multiple docking personnel according to the multiple personnel feature information sources, wherein each personnel docking feature data group includes personnel basic attribute data, personnel docking skill experience data and personnel docking service evaluation data corresponding to each docking personnel; Based on the bill connection feature analysis model, task allocation is performed on each installment bill information in the fourth installment bill information set to be processed according to the multiple personnel connection feature data groups, and multiple bill connection business allocation results are generated; Extracting multiple fixed personnel allocation business information corresponding to the multiple docking personnel according to the multiple personnel feature information sources, and updating the multiple fixed personnel allocation business information according to the multiple bill docking business allocation results to generate multiple personnel update allocation business information; Based on the updated allocation business information of the multiple personnel, the multiple bill docking business allocation results are balanced and optimized to obtain the docking business allocation result; Among them, based on the bill docking feature parsing model, tasks are assigned to each installment bill information in the fourth installment bill information set to be processed according to the multiple personnel docking feature data groups, and multiple bill docking business assignment results are generated, including: Extracting first installment bill information according to the fourth to-be-processed installment bill information set; The bill docking feature analysis model includes an input layer, a docking personnel demand feature analysis layer, a docking feature matching analysis layer and an output layer; Inputting the first installment bill information into the docking personnel demand feature analysis layer to obtain first bill docking personnel demand feature data; Based on the docking feature matching analysis layer, matching analysis is performed on the first bill docking personnel demand feature data and the multiple personnel docking feature data groups respectively to obtain multiple docking feature matching coefficients; Based on the multiple docking feature matching coefficients, selecting the optimal docking feature matching coefficient, and locating the multiple docking personnel based on the optimal docking feature matching coefficient to obtain a first bill docking service allocation result; By analogy, based on the bill connection feature parsing model, the fourth to-be-processed installment bill information set is continuously assigned tasks according to the multiple personnel connection feature data groups, and the multiple bill connection business assignment results are obtained in combination with the first bill connection business assignment result; The step of performing balanced optimization adjustment on the multiple bill docking business allocation results based on the multiple personnel updated allocation business information to obtain the docking business allocation results includes: Divide the multiple personnel update allocation service information into time zones based on preset time zone constraints to generate multiple time zone personnel allocation service groups; Based on the multiple time zone personnel allocation business groups, a time zone business allocation balance calculation is performed to obtain multiple time zone business allocation balance coefficients; When any one of the multiple time zone business allocation balancing coefficients does not satisfy the time zone business allocation balancing constraint, an abnormal time zone personnel allocation business result is generated; Performing a balanced adjustment based on the abnormal time zone personnel allocation business result to generate an adjusted personnel allocation business result; The plurality of bill docking business allocation results are optimized based on the adjustment personnel allocation business result to generate the docking business allocation result.

2. The method according to claim 1, characterized in that The first to-be-processed installment bill information set is authenticated based on the bill information quality constraint to generate a second to-be-processed installment bill information set, including: Performing data quality evaluation on the multiple installment bill information respectively to obtain multiple bill information quality evaluation coefficients; respectively judging whether the plurality of bill information quality evaluation coefficients satisfy the bill information quality constraint, and obtaining a plurality of bill information quality judgment results; Classifying the multiple installment bill information based on the multiple bill information quality judgment results to generate a normal installment bill information set and an abnormal installment bill information set; Sending the abnormal installment bill information set to the user interaction interface to obtain an abnormal bill user feedback result set; The abnormal installment bill information set is adjusted based on the abnormal bill user feedback result set to generate an adjusted installment bill information set, and the normal installment bill information set and the adjusted installment bill information set are added to the second installment bill information set to be processed.

3. The method according to claim 1, characterized in that Based on the multi-level bill user installment trust constraint, the second to-be-processed installment bill information set is subjected to a bill installment trust review to generate a third to-be-processed installment bill information set, including: Collecting the bill installment repayment record data of each user corresponding to the second to-be-processed installment bill information set, obtaining multiple user bill installment repayment record sets, and performing user bill installment repayment credit evaluation based on the multiple user bill installment repayment record sets to generate multiple user bill installment repayment credit coefficients; Collecting financial information of each user corresponding to the second to-be-processed installment bill information set to obtain multiple user financial information sets, and evaluating the user financial status based on the multiple user financial information sets to generate multiple user financial status coefficients; Based on the first weight distribution constraint condition, weighted calculations are performed on the multiple user bill installment repayment credit coefficients and the multiple user financial status coefficients to generate multiple user installment trust coefficients; Based on the multi-level bill user installment trust constraint, installment trusted user authentication is performed according to the multiple user installment trust coefficients to generate multiple installment trusted users; The installment bill information of the second to-be-processed installment bill information set is matched based on the multiple installment trusted users to obtain the third to-be-processed installment bill information set.

4. The method according to claim 1, characterized in that The fourth installment bill information set to be processed is generated by performing a processing sequence constraint based on the third installment bill information set to be processed, including: Extracting multiple installment bill time zone information according to the third to-be-processed installment bill information set, and performing time sequence priority evaluation based on the multiple installment bill time zone information to generate multiple bill time sequence priority coefficients; Obtaining multiple user attribute information corresponding to the third to-be-processed installment bill information set, and performing user priority evaluation based on the multiple user attribute information to generate multiple bill user priority coefficients; Based on the second weight distribution constraint condition, weighted calculation is performed on the multiple billing time sequence priority coefficients and the multiple billing user priority coefficients to generate multiple billing priority processing coefficients; The third to-be-processed installment bill information set is arranged in descending order based on the multiple bill priority processing coefficients to obtain the fourth to-be-processed installment bill information set.

5. Automatic docking management system for bill installment business, characterized in that: The system is used to perform any one of the methods described in claims 1 to 4, and the system comprises: A task acquisition unit, the task acquisition unit is used to load a first pending installment bill information set and a docking personnel database according to the bill installment business management module, wherein the first pending installment bill information set includes multiple installment bill information corresponding to multiple pending installment bills; A data authentication unit, the data authentication unit is used to perform data authentication on the first to-be-processed installment bill information set based on the bill information quality constraint to generate a second to-be-processed installment bill information set; A trust review unit, the trust review unit being used to establish a multi-level bill user installment trust constraint based on the second installment bill information set to be processed, and to perform a bill installment trust review on the second installment bill information set to be processed based on the multi-level bill user installment trust constraint to generate a third installment bill information set to be processed; A sequence processing unit, the sequence processing unit is used to process the sequence constraint based on the third to-be-processed installment bill information set to generate a fourth to-be-processed installment bill information set; A personnel feature acquisition unit, the personnel feature acquisition unit is used to perform feature recognition on the docking personnel database according to multi-dimensional personnel feature indicators to generate multiple personnel feature information sources, wherein the multi-dimensional personnel feature indicators include personnel basic attribute indicators, personnel docking skill experience indicators, personnel fixed allocation business indicators and personnel docking service evaluation indicators; A bill docking scheduling unit, which is used to perform task allocation based on a bill docking scheduling algorithm, according to the multiple personnel feature information sources and the fourth set of installment bill information to be processed, generate a docking business allocation result, and execute the installment bill docking management of the fourth set of installment bill information to be processed according to the docking business allocation result.

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