CN115564422A - Automatic butt-joint management method and system for bill staging service - Google Patents
Automatic butt-joint management method and system for bill staging service Download PDFInfo
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Abstract
The invention provides an automatic docking management method and system for a bill staging service, and relates to the technical field of borrow information processing.
Description
Technical Field
The invention relates to the technical field of borrow data information processing, in particular to a method and a system for automatic butt joint management of bill staging services.
Background
Along with the rapid development of a consumption platform, the consumption concept of people is influenced, in order to stimulate consumption, the payment can be carried out by carrying out bill staging, such as loan staging, payment staging and the like, the bill can be distributed to a plurality of time intervals by carrying out bill staging, the repayment pressure can be effectively relieved, generally speaking, the payment can be approved as long as the personal reputation is good, at present, the butt joint service distribution is mainly carried out manually, the targeted integrated management and control is carried out by carrying out data partitioning, and due to certain defects of the existing service butt joint management method, the service butt joint management capability can be optimized and improved by carrying out the management method.
In the prior art, due to insufficient intellectualization of the method for managing the docking of the staging services, the docking distribution process of the services is not perfect, so that the degree of conformity of the service docking results is insufficient, and the final management controllability and the follow-up service propulsion efficiency are influenced.
Disclosure of Invention
The application provides an automatic docking management method and system for a bill staging service, which are used for solving the technical problems that due to insufficient intellectualization and imperfect docking distribution flow of the service, the conformity of service docking results is insufficient, and the final management controllability and the follow-up service propulsion efficiency are influenced in the docking management method for the staging service in the prior art.
In view of the above problems, the present application provides an automatic docking management method and system for a bill staging service.
In a first aspect, the present application provides a method for automatic docking management of a bill staging service, where the method includes: acquiring basic information of the installments bills; acquiring a time period attribute of the installments bill according to the basic information, and performing multi-batch docking constraint through the time period attribute to generate a multi-batch docking task; acquiring personnel attribute information and task information to be handled of the butt joint personnel; performing task allocation of the multi-batch docking tasks according to the personnel attribute information and the to-be-handled task information to obtain task allocation results; obtaining historical service data of the butt joint personnel, and extracting personnel adaptation characteristics of the butt joint personnel through the historical service data to obtain personnel adaptation characteristic extraction results; and performing task allocation optimization of the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and performing docking management of the stage bill service through the optimized task allocation result.
In a second aspect, the present application provides an automatic docking management system for a bill staging service, the system including: the information acquisition module is used for acquiring basic information of the obtained installments bill; the task generation module is used for obtaining the time period attribute of the installments bill according to the basic information, and carrying out multi-batch docking constraint through the time period attribute to generate multi-batch docking tasks; the information acquisition module is used for acquiring personnel attribute information and task information to be handled of the butt joint personnel; the task allocation module is used for performing task allocation of the multi-batch docking tasks according to the personnel attribute information and the to-be-handled task information to obtain a task allocation result; the characteristic extraction module is used for obtaining historical service data of the butt joint personnel, extracting personnel adaptation characteristics of the butt joint personnel through the historical service data and obtaining personnel adaptation characteristic extraction results; and the result optimization module is used for performing task allocation optimization on the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and performing docking management on the installbill service through the optimized task allocation result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the automatic docking management method for the bill staging service, provided by the embodiment of the application, is used for acquiring basic information of a stage bill to determine a time period attribute of the stage bill, performing multi-batch docking constraint on the basis of the time period attribute to generate multi-batch docking tasks, acquiring personnel attribute information and task information to be handled of a dockee, performing task allocation of the multi-batch docking tasks on the basis of the multi-batch docking tasks, acquiring a task allocation result, acquiring historical service data of the dockee, performing personnel adaptation feature extraction on the dockee on the basis of the historical service data, acquiring a personnel adaptation feature extraction result, further performing task allocation optimization on the task allocation result on the basis of the personnel adaptation feature extraction result, acquiring an optimized task allocation result, further performing docking management on the stage bill service, solving the technical problems that the controllability of the service allocation flow is not perfect due to the insufficient intellectualization in the docking management method for the stage bill service in the prior art, the conformity of the service docking management result is not enough, the final management controllability of management is not enough, and the service pushing efficiency is automatically and the service management is completed by optimizing the service docking management flow.
Drawings
Fig. 1 is a schematic flow chart of an automatic docking management method for a bill staging service according to the present application;
fig. 2 is a schematic view illustrating a multi-batch docking task generation flow in an automatic docking management method for a bill staging service according to the present application;
fig. 3 is a schematic diagram illustrating a flow of acquiring personnel attribute information in an automatic docking management method for a bill staging service according to the present application;
fig. 4 is a schematic structural diagram of an automatic docking management system for a bill staging service according to the present application.
Description of reference numerals: the system comprises an information acquisition module 11, a task generation module 12, an information acquisition module 13, a task allocation module 14, a feature extraction module 15 and a result optimization module 16.
Detailed Description
The application provides an automatic docking management method and system for a bill staging service, wherein a time period attribute of the staging bill is determined based on basic information of the staging bill, multi-batch docking constraint is carried out to generate multi-batch docking tasks, personnel attribute information of a dockee and task information to be handled are obtained to carry out task allocation, historical service data are collected to carry out personnel adaptation feature extraction, an extraction result is determined to carry out task allocation result optimization, and docking management of the staging bill service is carried out based on an optimization result.
Example one
As shown in fig. 1, the present application provides an automatic docking management method for a bill staging service, the method including:
step S100: acquiring basic information of the installments bills;
step S200: acquiring a time period attribute of the installments bill according to the basic information, and performing multi-batch docking constraint through the time period attribute to generate a multi-batch docking task;
specifically, by carrying out bill staging, a bill can be shared into a plurality of time intervals, and repayment pressure can be effectively relieved.
Further, the basic information is acquired by collecting the information of the installment bill, the complete period of the bill installment is determined based on the basic information, a fixed single repayment constraint time interval is further determined and is used as the time period attribute, the client can determine the actual repayment time in the time period attribute for repayment, the actual repayment time belongs to the normal category, the task butt joint constraint is further performed based on the time period attribute, the butt joint tasks are divided and classified, the centralized management and control are facilitated, and the multi-batch butt joint tasks are generated.
Further, as shown in fig. 2, step S200 of the present application further includes:
step S210: obtaining actual customer repayment date of the installled bill;
step S220: carrying out payment cycle distribution according to the actual customer payment date to obtain a payment date distribution result;
step S230: generating a distribution probability constraint parameter according to the repayment date distribution result;
step S240: and carrying out multi-batch docking constraint according to the distribution probability constraint parameters and the time period attributes to generate multi-batch docking tasks.
Specifically, the installment bill determines an actual repayment date based on a plurality of periods, wherein the actual client repayment date may be a historical repayment date of the client, and may be used as reference information to perform subsequent repayment date probability analysis, the actual client repayment date is within the limit range of the time period attribute, the actual repayment period is analyzed based on the actual client repayment date, for example, the actual repayment date of the client is limited to one number and has a certain regularity, and may be used as the repayment date distribution result of the client, the dockee may dock a plurality of target clients, may perform traffic partitioning based on the repayment date distribution result, perform comprehensive evaluation analysis on the repayment date distribution result, evaluate the repayment probability of each date based on the repayment date of each period in the actual client repayment date, further perform subsequent repayment date prediction, generate the distribution probability constraint parameter, the distribution constraint parameter corresponds to a plurality of target clients one by one, further determine the batch probability constraint parameter corresponding to the batch probability distribution parameter in the time period attribute, perform sequential repayment based on the task progress, and perform task planning based on the batch progress of the task.
Step S300: acquiring personnel attribute information and task information to be handled of butt joint personnel;
step S400: performing task allocation of the multi-batch docking tasks according to the personnel attribute information and the to-be-handled task information to obtain task allocation results;
specifically, determining the time of entry of the docking personnel, acquiring corresponding working time limit as working time data, calling task completion information of the docking personnel, determining corresponding task time and specific task information, determining task quantity data, increasing the corresponding task quantity data in a forward direction along with the improvement of proficiency of the service of the docking personnel, and determining task quantity constraint data through task quantity increase analysis; the authority of the butt joint personnel is called, authority constraint data are determined based on a corresponding service range, and the task quantity constraint data and the authority constraint data are used as personnel attribute information; further determining the specific service information of the multi-batch docking tasks, analyzing repayment credits of corresponding clients, judging whether overdue repayment and other conditions exist, determining the degree of cooperation of the clients, generating the task information to be handled, further performing adaptability analysis on the personnel attribute information and the task information to be handled, determining an adaptability analysis result, ensuring that the docking personnel have the authority of independently propelling a service process and have a time window period, and acquiring the task allocation result, wherein the task allocation result is a primary allocation result, the condition that the multiple docking personnel are matched with the docking tasks possibly exists, and the acquisition of the task allocation result provides a basic basis for optimizing the subsequent allocation result.
Further, as shown in fig. 3, step S300 of the present application further includes:
step S310: acquiring data of the task completion amount and the working time of the butt joint personnel;
step S320: performing task quantity increase evaluation according to the completed task quantity data and the working time data to obtain increase task quantity constraint data;
step S330: acquiring personnel authority information of the butt joint personnel, and generating authority constraint data according to the personnel authority information;
step S340: and acquiring the personnel attribute information according to the constraint data of the growth task amount and the constraint data of the authority.
Specifically, a person in charge of docking the installment bill is determined, the time of entry of the dockee is determined, the work time data is obtained, the services completed within the time of entry of the dockee are measured, the task amount data is obtained, the work time data is divided into a plurality of time intervals by time node division, the time intervals are mapped and correspond to the task amount data, the sequential arrangement is performed based on the time sequence, the dockee is subjected to task amount growth evaluation along with the time passage, the task amount comparison is performed on adjacent time nodes, the task amount growth data is determined, the growth rate evaluation is further performed based on the task amount growth data, the growth task amount constraint data is generated, the person authority is called, the information of the dockee, such as the task level and the decision range, which can be charged by the dockee is determined, the authority constraint data is generated based on the basis, the growth task amount constraint data and the authority constraint data are further integrated, the person attribute information is generated, and the degree of engagement between the person attribute information and the dockee can be guaranteed.
Further, step S320 of the present application further includes:
step S321: setting a working time node threshold according to the big data;
step S322: performing time division on the working time data based on the working time node threshold to obtain a time division result, wherein the time division result is provided with a node threshold identifier;
step S323: and performing task quantity growth analysis on the task quantity data according to the time division result to obtain growth task quantity constraint data.
Specifically, the working time data of the docking personnel is determined by collecting the time of entry of the docking personnel, the working time node threshold value, namely the interval critical value for working time division, is set based on big data, the working time data is subjected to time division based on the working time node threshold value, a plurality of time division nodes are determined, further, threshold identification is carried out on the plurality of time division nodes, the time division result is obtained, the time division result and the task quantity data are mapped and correspond to each other, the task quantity data corresponding to each time node in the time division result is subjected to increase limit analysis based on time sequence, the corresponding task quantity increase rate is determined, the constraint data of the increased task quantity are further determined, and the constraint data of the increased task quantity are used as the judgment basis of the personnel attribute information.
Further, step S400 of the present application further includes:
step S410: obtaining docking requirement information of the docking personnel;
step S420: performing requirement rationality analysis according to the docking requirement information, and generating requirement credible identification information based on a requirement rationality analysis result;
step S430: and performing task allocation of the multi-batch docking tasks according to the requirement credible identification information, the docking requirement information, the personnel attribute information and the to-be-handled task information to obtain the task allocation result.
Specifically, limiting conditions required for completing task docking are determined, such as customer credibility and the like, and are used as the docking requirement information of the docking personnel, the docking requirement information is subjected to reasonability analysis, whether the information reaches the standard or not and the probability of normal propulsion of a subsequent task are determined, the reasonability analysis result is determined, the subsequent task process can be effectively guaranteed to be within a controllable range through the reasonability analysis of the information, an unexpected condition is avoided, the reasonability information in the docking requirement information and the corresponding analysis result are correspondingly marked, the requirement credible marking information is generated, further, the requirement credible marking information, the docking requirement information, the personnel attribute information and the to-be-handled task information are subjected to association matching analysis, exemplarily, a matching level threshold value can be set, when the matching degree of each group of information meets the set matching level threshold value, the information matching level reaches the standard, the normal propulsion of a task can be performed, and the task allocation result is generated on the basis that the task allocation result is performed.
Step S500: obtaining historical service data of the butt joint personnel, and extracting personnel adaptation characteristics of the butt joint personnel through the historical service data to obtain personnel adaptation characteristic extraction results;
step S600: and performing task allocation optimization of the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and performing docking management of the stage bill service through the optimized task allocation result.
Specifically, historical business data is extracted from the docking personnel, the historical business data is business data which is completed by the docking personnel once, customers corresponding to each group of business data are determined, business quality assessment is performed to determine business completion quality, comprehensive assessment is performed to determine personnel adaptation characteristics of the docking personnel, the personnel adaptation characteristics are types of the customers which the docking personnel are good at docking, for example, the customers are difficult to dock, overdue and the like, timely communication is needed to ensure that a repayment process is normal, the docking personnel need to have high interpersonal communication capacity, differences of the personnel adaptation characteristics can effectively maintain different types of customers, corresponding identification is performed on the docking personnel and the personnel adaptation characteristics so as to perform identification and differentiation later, personnel adaptation characteristic extraction results are generated, task allocation results are further adjusted based on the personnel adaptation characteristic extraction results, the degree of agreement between the docking personnel and the customers is improved, the optimized task allocation results are generated, docking management of the deferred services is further performed based on the optimized task allocation results, the docking personnel and the customers are guaranteed, normal utilization of the docking personnel and the customers is realized, the efficiency of the customer is improved, the efficiency of the effective management is improved, and the control of the customer is improved, and the efficiency of the customer allocation of the bill is improved.
Further, step S600 of the present application further includes:
step S610-1: performing adaptation association analysis on the client and the butt personnel according to the historical service data to obtain an adaptation association analysis result;
step S620-1: carrying out adaptation grading according to the adaptation correlation analysis result to obtain adaptation correlation grade data;
step S630-1: and performing task allocation optimization on the task allocation result according to the adaptation association grade data and the personnel adaptation feature extraction result to obtain the optimized task allocation result.
Specifically, historical business data of the dockee is retrieved, the dockee and the client are subjected to adaptive association analysis based on the historical business data, it is determined that the dockee who has cooperated with each user exists in the historical business data, the dockee and the corresponding historical business data are subjected to mapping correspondence, adaptive association analysis is performed based on the mapping result, an adaptive association analysis result is obtained, adaptation grade evaluation is further performed based on the adaptive association analysis result, the higher the number of historical cooperative times of the dockee and the client is, the higher the client satisfaction degree is, the higher the adaptive grade of the dockee and the client is indicated, the adaptive association grade data is generated, further, task assignment optimization is performed on the task assignment result based on the adaptive association grade data and the human adaptive characteristic extraction result, wherein the human adaptive characteristic extraction result is a client type characteristic which is determined to be suitable for the dockee based on the dockee side, the adaptive association grade data is client adaptive information matching data determined based on the client, the task assignment result is a primary task assignment result, the optimization assignment result is obtained, an exemplary existence of the human dockee and the adaptive association grade data can be determined based on the client adaptive association grade data, the client assignment result is increased by comparing the highest priority of the dockee and the client optimization result, the client assignment accuracy is improved.
Further, step S600 of the present application further includes:
step S610-2: judging whether an abnormal task exists or not;
step S620-2: when an abnormal task exists, generating time constraint data according to the task attribute of the abnormal task;
step S630-2: and performing task distribution of the abnormal task according to the time constraint data.
Specifically, task exception analysis is performed on a plurality of butt-joint tasks included in the optimized task allocation result, whether an abnormal task exists is judged, when the abnormal task does not exist, follow-up business promotion is normally performed on the basis of the task optimized allocation result, when the abnormal task exists, task exception degree and exception state analysis is performed on the abnormal task, task attributes of the abnormal task are obtained, for example, the actual repayment date fluctuates too much, multiple repayment overdue exists, regularity does not exist, and the like, so that the controllability of the installment management is influenced, the abnormal task is comprehensively evaluated to generate time constraint data, namely a limited time interval of the repayment date, and the abnormal task is distributed on the basis of the time constraint data, so that potential management and control risks can be effectively avoided, and installment management imbalance is caused.
Example two
Based on the same inventive concept as the automatic docking management method of the bill staging service in the foregoing embodiment, as shown in fig. 4, the present application provides an automatic docking management system of the bill staging service, where the system includes:
the information acquisition module 11 is used for acquiring basic information of the obtained installment bill;
the task generating module 12 is configured to obtain a time period attribute of the installment bill according to the basic information, and perform multi-batch docking constraint according to the time period attribute to generate a multi-batch docking task;
the information acquisition module 13 is used for acquiring personnel attribute information and task information to be handled of the butt joint personnel;
the task allocation module 14 is configured to perform task allocation on the multiple batches of docking tasks according to the personnel attribute information and the to-do task information, and obtain a task allocation result;
the feature extraction module 15 is configured to obtain historical service data of the docking staff, perform staff adaptation feature extraction on the docking staff according to the historical service data, and obtain a staff adaptation feature extraction result;
and the result optimization module 16 is configured to perform task allocation optimization on the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and perform docking management on the installment bill service through the optimized task allocation result.
Further, the system further comprises:
the data acquisition module is used for acquiring the data of the task completion amount and the working time of the butt joint personnel;
the task amount constraint module is used for performing task amount increase evaluation according to the completed task amount data and the working time data to obtain increased task amount constraint data;
the authority constraint module is used for acquiring personnel authority information of the butt joint personnel and generating authority constraint data according to the personnel authority information;
and the attribute information acquisition module is used for acquiring the personnel attribute information according to the constraint data of the growth task amount and the constraint data of the authority.
Further, the system further comprises:
the threshold setting module is used for setting a working time node threshold according to the big data;
the time division module is used for carrying out time division on the working time data based on the working time node threshold value to obtain a time division result, wherein the time division result is provided with a node threshold value identifier;
and the data analysis module is used for performing task quantity growth analysis on the task quantity data according to the time division result to obtain the growth task quantity constraint data.
Further, the system further comprises:
the date acquisition module is used for acquiring the actual client repayment date of the installled bill;
the date distribution module is used for carrying out payment period distribution according to the actual client payment date to obtain a payment date distribution result;
the parameter generation module is used for generating a distribution probability constraint parameter according to the repayment date distribution result;
and the task generation module is used for carrying out multi-batch docking constraint according to the distribution probability constraint parameters and the time period attributes to generate multi-batch docking tasks.
Further, the system further comprises:
the demand information acquisition module is used for acquiring docking demand information of the docking personnel;
the information analysis module is used for carrying out requirement reasonability analysis according to the butt joint requirement information and generating requirement credible identification information based on a requirement reasonability analysis result;
and the docking task allocation module is used for performing task allocation on the multi-batch docking tasks through the requirement trusted identification information, the docking requirement information, the personnel attribute information and the task information to be handled to obtain the task allocation result.
Further, the system further comprises:
the data association analysis module is used for carrying out adaptation association analysis on the client and the butt personnel according to the historical service data to obtain an adaptation association analysis result;
the association grade determining module is used for carrying out adaptation grading according to the adaptation association analysis result to obtain adaptation association grade data;
and the distribution result optimization module is used for performing task distribution optimization on the task distribution result according to the adaptation association grade data and the personnel adaptation characteristic extraction result to obtain the optimized task distribution result.
Further, the system further comprises:
the abnormal task judging module is used for judging whether an abnormal task exists or not;
the constraint data generation module is used for generating time constraint data according to task attributes of an abnormal task when the abnormal task exists;
and the abnormal task distribution module is used for performing task distribution on the abnormal tasks according to the time constraint data.
In the present specification, through the foregoing detailed description of the method for managing automatic docking of a bill staging service, those skilled in the art can clearly know that, in the present embodiment, the method and system for managing automatic docking of a bill staging service are provided.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An automatic docking management method for a bill staging service, the method comprising:
acquiring basic information of the installments bills;
acquiring a time period attribute of the installments bill according to the basic information, and performing multi-batch docking constraint through the time period attribute to generate a multi-batch docking task;
acquiring personnel attribute information and task information to be handled of the butt joint personnel;
performing task allocation of the multi-batch docking tasks according to the personnel attribute information and the to-be-handled task information to obtain task allocation results;
obtaining historical service data of the butt joint personnel, and extracting personnel adaptation characteristics of the butt joint personnel through the historical service data to obtain personnel adaptation characteristic extraction results;
and performing task allocation optimization of the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and performing docking management of the stage bill service through the optimized task allocation result.
2. The method of claim 1, wherein the method further comprises:
acquiring data of the task completion amount and the working time of the butt joint personnel;
performing task quantity increase evaluation according to the completed task quantity data and the working time data to obtain increase task quantity constraint data;
acquiring personnel authority information of the butt joint personnel, and generating authority constraint data according to the personnel authority information;
and acquiring the personnel attribute information according to the constraint data of the growth task amount and the constraint data of the authority.
3. The method of claim 2, wherein the method further comprises:
setting a working time node threshold according to the big data;
performing time division on the working time data based on the working time node threshold to obtain a time division result, wherein the time division result is provided with a node threshold identifier;
and performing task quantity growth analysis on the task quantity data according to the time division result to obtain growth task quantity constraint data.
4. The method of claim 1, wherein the method comprises:
obtaining an actual customer repayment date for the installment bill;
carrying out payment period distribution according to the actual customer payment date to obtain a payment date distribution result;
generating a distribution probability constraint parameter according to the repayment date distribution result;
and carrying out multi-batch docking constraint according to the distribution probability constraint parameters and the time period attributes to generate multi-batch docking tasks.
5. The method of claim 1, wherein the method comprises:
obtaining docking requirement information of the docking personnel;
performing requirement rationality analysis according to the docking requirement information, and generating requirement credible identification information based on a requirement rationality analysis result;
and performing task allocation of the multi-batch docking tasks according to the requirement credible identification information, the docking requirement information, the personnel attribute information and the to-be-handled task information to obtain the task allocation result.
6. The method of claim 1, wherein the method comprises:
performing adaptation association analysis of the client and the butt personnel according to the historical service data to obtain an adaptation association analysis result;
carrying out adaptation grading according to the adaptation correlation analysis result to obtain adaptation correlation grade data;
and performing task allocation optimization on the task allocation result according to the adaptation association grade data and the personnel adaptation characteristic extraction result to obtain an optimized task allocation result.
7. The method of claim 1, wherein the method comprises:
judging whether an abnormal task exists or not;
when an abnormal task exists, generating time constraint data according to the task attribute of the abnormal task;
and performing task distribution of the abnormal task according to the time constraint data.
8. An automatic docking management system for a billing staging service, the system comprising:
the information acquisition module is used for acquiring basic information of the obtained installments bill;
the task generation module is used for obtaining the time period attribute of the installments bill according to the basic information, and carrying out multi-batch docking constraint through the time period attribute to generate multi-batch docking tasks;
the information acquisition module is used for acquiring personnel attribute information and task information to be handled of the butt joint personnel;
the task allocation module is used for performing task allocation of the multi-batch docking tasks according to the personnel attribute information and the to-be-handled task information to obtain a task allocation result;
the characteristic extraction module is used for obtaining historical service data of the butt joint personnel, extracting personnel adaptation characteristics of the butt joint personnel through the historical service data and obtaining personnel adaptation characteristic extraction results;
and the result optimization module is used for performing task allocation optimization on the task allocation result according to the personnel adaptation feature extraction result to obtain an optimized task allocation result, and performing docking management on the installbill service through the optimized task allocation result.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652396A (en) * | 2019-12-09 | 2020-09-11 | 武汉空心科技有限公司 | Task allocation method for designated user of working platform |
| CN113343058A (en) * | 2021-05-31 | 2021-09-03 | 平安普惠企业管理有限公司 | Voice session supervision method and device, computer equipment and storage medium |
| CN114091941A (en) * | 2021-11-26 | 2022-02-25 | 中国建设银行股份有限公司 | Task allocation method and device, electronic equipment and storage medium |
| CN114936779A (en) * | 2022-05-30 | 2022-08-23 | 中国银行股份有限公司 | Task allocation method and device |
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2022
- 2022-10-21 CN CN202211298205.3A patent/CN115564422A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652396A (en) * | 2019-12-09 | 2020-09-11 | 武汉空心科技有限公司 | Task allocation method for designated user of working platform |
| CN113343058A (en) * | 2021-05-31 | 2021-09-03 | 平安普惠企业管理有限公司 | Voice session supervision method and device, computer equipment and storage medium |
| CN114091941A (en) * | 2021-11-26 | 2022-02-25 | 中国建设银行股份有限公司 | Task allocation method and device, electronic equipment and storage medium |
| CN114936779A (en) * | 2022-05-30 | 2022-08-23 | 中国银行股份有限公司 | Task allocation method and device |
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