CN117893334B - Insurance task allocation method and system based on big data - Google Patents
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Abstract
The application provides an insurance task allocation method and system based on big data, wherein the method comprises the following steps: generating a policy task set in response to a user request; generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; and determining target personnel of the target task in the target object group. The application improves the distribution efficiency of the insurance products, so that the insurance products can be accurately distributed to experienced personnel, thereby improving the transaction rate of the insurance products.
Description
Technical Field
The application relates to the field of big data, in particular to an insurance task allocation method and system based on big data.
Background
At present, the requirements of the insurance products cannot be matched with the sales height in the market, time difference exists between the insurance products and corresponding product responsible persons, the corresponding product responsible persons can be informed through a large amount of time transfer or notification, the consumed time in the middle often causes users to select the insurance products of other enterprises or give up the insurance products due to the fact that the service is not in place, and experienced processing personnel cannot be accurately positioned through the insurance products.
Disclosure of Invention
In view of the foregoing, the present application has been made to provide a big data based insurance task allocation method and system which overcomes the foregoing problems or at least partially solves the foregoing problems, including:
an insurance task allocation method based on big data, the method comprising:
generating a policy task set in response to a user request;
Generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money;
Generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed;
Determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data;
and determining target personnel of the target task in the target object group.
Further, the step of generating a scheduling task list according to the policy task set and the priority degradation order comprises the following steps:
the insurance policy task set is subjected to text analysis and decomposition into a plurality of to-be-continued insurance policy tasks and a plurality of newly signed insurance policy tasks;
Determining the renewal time of each to-be-guaranteed policy task, and sequencing a plurality of to-be-guaranteed policy tasks from short to long according to the length of the renewal time to generate a first task flow; the method comprises the steps of obtaining the amount of a policy of each new signing and ordering policy task, and ordering a plurality of new signing and ordering policy tasks from large to small according to the amount of the policy to generate a second task stream;
And generating the scheduling task list according to the first task stream and the second task stream.
Further, the step of generating the same task processing set of each risk according to the first task flow and the second task flow and the same risk includes:
dividing the first task flow and the second task flow into task processing blocks with different priorities, wherein the task processing blocks comprise a first priority processing task block, a second priority processing task block, a first post-processing task block and a second post-processing task block;
Acquiring each priority processing task with the same characteristic attribute from the first priority processing task block and the second priority processing task block, and generating a priority task processing set through each priority processing task; acquiring each post-processing task with the same characteristic attribute according to the first post-processing task block and the second post-processing task block, and generating a post-processing task processing set through each post-processing task; wherein the characteristic attribute is an insurance type;
and generating the task processing set according to the priority task processing set and the deferred task processing set.
Further, the step of determining the target object group in the preset policy allocation model according to the task processing set includes:
Acquiring historical task information from the preset policy allocation model; the history task information at least comprises a plurality of history processing tasks and target dangerous types corresponding to the history processing tasks; each of the historical processing tasks corresponds to at least one processing object;
And determining a target object group according to the historical task information and the task processing set.
Further, the step of determining a target object group according to the historical task information and the task processing set includes:
Acquiring the historical processing time length of each processing object from the historical task information;
Determining the average processing duration of each processing object according to the historical processing duration of each processing object;
Determining the matching degree corresponding to each processing object according to the average processing duration and the completion cycle time corresponding to each task in the task processing set;
Determining a plurality of target processing objects in each processing object according to the matching degree and a preset screening proportion;
and generating the target object group according to the target processing objects.
Further, the step of generating the preset policy allocation model from the historical processing data includes:
Acquiring historical processing data of historical processing personnel on the historical insurance policies, wherein the historical processing data comprises historical insurance policy amounts, historical problem rates and historical success rates corresponding to each historical insurance policy, and the historical success rates comprise renewal success rates and signing success rates;
and generating the preset policy allocation model through the historical policy amount, the historical problem rate and the historical success rate.
Further, the step of determining the target person of the target task in the target object group includes:
Determining whether each target object of the target object group is in an incumbent state;
screening the target object group to obtain a first target object set in an incumbent state;
acquiring task processing conditions of each target user in the first target object set, wherein the task processing conditions comprise task weights and task numbers;
Determining idle users in the first target object set according to the task weight and the task number;
generating a second target object set according to the idle user;
Acquiring professional evaluation values of all the processors in the second target object set, wherein the professional evaluation values are obtained through the historical problem rate and the historical success rate of the processors;
sorting each processor according to the values according to the professional evaluation value to generate an allocation priority table;
And sequentially distributing the target tasks to the target persons corresponding to the high-low tasks in the distribution priority table.
The application also discloses an insurance task distribution system based on big data, which comprises:
the first generation module is used for responding to the user request to generate a policy task set;
The second generation module is used for generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow used for representing the priority renewal and a second task flow used for representing the priority policy amount;
the third generation module is used for generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed;
the first determining module is used for determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data;
And the second determining module is used for determining target personnel of a target task in the target object group.
The application also discloses a computer device, which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the insurance task allocation method based on big data when being executed by the processor.
The application also discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the insurance task allocation method based on big data when being executed by a processor.
The application has the following advantages:
In the embodiment of the application, compared with the prior art that the time consumed in the middle often causes a user to select insurance products of other enterprises or discard the insurance products due to insufficient service, and experienced processing personnel cannot be accurately positioned through the insurance products, the application provides a solution of a big data-based insurance task allocation method and system, which specifically comprises the following steps: "big data based insurance task allocation method, the method includes: generating a policy task set in response to a user request; generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; and determining target personnel of a target task in the target object group. Generating a scheduling task list by arranging according to the priority degradation sequence according to the policy task set; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy allocation model according to the task processing set; the problem that a target person for determining a target task in the target object group "solves the problem that the time consumed in the middle often causes a user to select insurance products of other enterprises or discard the insurance products due to insufficient service, and experienced processing personnel cannot be accurately positioned through the insurance products" is solved, so that the effect of improving the distribution efficiency of the insurance products, enabling the insurance products to be accurately distributed to the experienced personnel, and improving the transaction rate of the insurance products is achieved.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the present application will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for assigning insurance tasks based on big data according to an embodiment of the present application;
FIG. 2 is a block diagram of an insurance task distribution system based on big data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order that the manner in which the above recited objects, features and advantages of the present application are obtained will become more readily apparent, a more particular description of the application briefly described above will be rendered by reference to the appended drawings. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, the requirements of the insurance products cannot be matched with the sales height in the market, time difference exists between the insurance products and corresponding product responsible persons, the corresponding product responsible persons can be informed through a large amount of time transfer or notification, the consumed time in the middle often causes users to select the insurance products of other enterprises or give up the insurance products due to the fact that the service is not in place, and experienced processing personnel cannot be accurately positioned through the insurance products.
Referring to fig. 1, a flowchart illustrating steps of a method for assigning insurance tasks based on big data according to an embodiment of the present application is shown;
an insurance task allocation method based on big data, the method comprising:
s110, generating a policy task set in response to a user request;
S120, arranging and generating a scheduling task list according to the policy task set and the priority degradation order, wherein the scheduling task list comprises a first task flow used for representing priority renewal and a second task flow used for representing priority policy amount;
S130, generating task processing sets with the same risk types according to the first task flow and the second task flow and the same risk types;
S140, determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data;
s150, determining target personnel of a target task in the target object group.
In the embodiment of the application, compared with the prior art that the time consumed in the middle often causes a user to select insurance products of other enterprises or discard the insurance products due to insufficient service, and experienced processing personnel cannot be accurately positioned through the insurance products, the application provides a solution of a big data-based insurance task allocation method and system, which specifically comprises the following steps: "big data based insurance task allocation method, the method includes: generating a policy task set in response to a user request; generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; and determining target personnel of a target task in the target object group. Generating a scheduling task list by arranging according to the priority degradation sequence according to the policy task set; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy allocation model according to the task processing set; the problem that a target person for determining a target task in the target object group "solves the problem that the time consumed in the middle often causes a user to select insurance products of other enterprises or discard the insurance products due to insufficient service, and experienced processing personnel cannot be accurately positioned through the insurance products" is solved, so that the effect of improving the distribution efficiency of the insurance products, enabling the insurance products to be accurately distributed to the experienced personnel, and improving the transaction rate of the insurance products is achieved.
Next, an insurance task allocation method based on big data in the present exemplary embodiment will be further described.
It should be noted that, the policy task set forms policy tasks according to the needs of the user, where the needs of the user include first purchase insurance, renewal, conversion insurance, etc., and the dangerous seeds in the policy tasks include financial insurance, engineering insurance, car insurance, responsibility insurance, ship insurance, freight insurance, financial insurance, credit insurance, insurance, agricultural insurance, accident insurance, medical insurance, serious illness, life insurance, child education insurance, senior insurance, group insurance.
The step S120 is configured to generate a scheduled task list according to the policy task set and in a priority degradation order, where the scheduled task list includes a first task flow for indicating a priority duration and a second task flow for indicating a priority policy amount.
In an embodiment of the present invention, the specific process of "generating a scheduled task list according to the policy task set and arranged in a priority degradation order" in step S120 may be further described in the following description, where the scheduled task list includes a first task flow for indicating a priority renewal and a second task flow for indicating a priority policy amount.
As will be described in the following steps,
S210, carrying out text analysis and decomposition on the policy task set to obtain a plurality of to-be-continued policy tasks and a plurality of newly signed policy tasks;
S220, determining the duration of each to-be-ensured policy task, and sequencing a plurality of to-be-ensured policy tasks from short to long according to the duration of the duration to generate a first task flow; the method comprises the steps of obtaining the amount of a policy of each new signing and ordering policy task, and ordering a plurality of new signing and ordering policy tasks from large to small according to the amount of the policy to generate a second task stream;
S230, generating the scheduling task list according to the first task stream and the second task stream.
It should be noted that the text analysis includes TF-IDF algorithm, textRank algorithm, word2Vec model, named entity recognition, K-means model, hierarchical clustering model, dbscan model, etc.; and analyzing each policy task in the policy task set through text analysis, extracting key information, and decomposing the key information into a to-be-continued policy task and a newly signed policy task according to the key information.
As an example, when the key information is the policy expiration time, that is, the renewal time, the policy expiration time is compared with the current time, and the policy task to be continued closer to the policy expiration time is determined to have a higher priority, and the policy task to be continued farther from the policy expiration time is determined to have a lower priority; and sequentially sequencing the to-be-renewed policy tasks from high priority to low priority, so as to generate a first task flow for representing the priority renewal.
As an example, when the key information is the first signing user, the amount of the policy in the policy corresponding to the first signing user is obtained, and the newly signed policy tasks are sequentially ordered according to the big or small amount of the policy, so as to generate a second task flow for representing the amount of the priority policy.
In a specific implementation, the key information may also be other attributes of the policy, such as gender, region, age, etc., and the corresponding task flows are formed by performing corresponding sorting through the features.
And as shown in the step S130, generating the same task processing set of each risk according to the same risk according to the first task flow and the second task flow.
In an embodiment of the present invention, the specific process of "generating the same task processing set according to the same risk category according to the first task flow and the second task flow" in step S130 may be further described in conjunction with the following description.
As will be described in the following steps,
S310, dividing the first task stream and the second task stream into task processing blocks with different priorities, wherein the task processing blocks comprise a first priority processing task block, a second priority processing task block, a first post-processing task block and a second post-processing task block;
S320, acquiring each priority processing task with the same characteristic attribute from the first priority processing task block and the second priority processing task block, and generating a priority task processing set through each priority processing task; acquiring each post-processing task with the same characteristic attribute according to the first post-processing task block and the second post-processing task block, and generating a post-processing task processing set through each post-processing task; wherein the characteristic attribute is an insurance type;
s330, generating the task processing set according to the priority task processing set and the deferred task processing set.
The task processing block groups together a certain amount of policy tasks of the same characteristic attribute and corresponding priority in two different types of policy task types.
As an example, the newspaper tasks with the same risk are screened out from the first task stream and the second task stream, and then the newspaper tasks with the same risk are divided into task blocks with a plurality of processing attributes according to the priority degree and the preset quantity limit, wherein the processing attributes are used for indicating the priority degree of the corresponding task blocks, and the priority degree comprises priority processing and delay processing.
In a specific implementation, in the allocation process, the task blocks with priority degree being processed preferentially are allocated preferentially, wherein the task blocks with priority degree also comprise a plurality of task blocks with priority degree, so that the task blocks with priority degree are required to be ordered again, and in the subsequent allocation, the task blocks with priority degree ordered in the first position, namely the first priority processing block, are screened and allocated.
In step S140, a target object group is determined in a preset policy distribution model according to the task processing set.
In an embodiment of the present invention, the specific process of "determining the target object group in the preset policy allocation model according to the task processing set" in step S140 may be further described in conjunction with the following description.
As will be described in the following steps,
S410, acquiring historical task information from the preset policy allocation model; the history task information at least comprises a plurality of history processing tasks and target dangerous types corresponding to the history processing tasks; each of the historical processing tasks corresponds to at least one processing object;
s420, determining a target object group according to the historical task information and the task processing set.
The history task information of the past processing object in the policy distribution model is preset, so that the specific task content and the specific dangerous seed corresponding to the policy task processed by the past processing object can be obtained.
As one example, a portion of the target objects in the set are screened by combining specific task content and specific risk types with task processing and a target object group is formed.
In a specific implementation, the target object group may be a team belonging to a certain region, or may be composed of target objects screened from a plurality of different regions.
As described in the step S420, a target object group is determined according to the historical task information and the task processing set.
In one embodiment of the present invention, the specific process of determining the target object group according to the historical task information and the task processing set in step S420 may be further described in conjunction with the following description.
As will be described in the following steps,
S510, acquiring the historical processing time length of each processing object from the historical task information;
s520, determining the average processing duration of each processing object according to the historical processing duration of each processing object;
s530, determining the matching degree corresponding to each processing object according to the average processing duration and the completion cycle time corresponding to each task in the task processing set;
s540, determining a plurality of target processing objects in each processing object according to the matching degree and a preset screening proportion;
s550, generating the target object group according to a plurality of the target processing objects.
The historical processing duration refers to time information consumed by the corresponding processing object in the past processing of the corresponding policy, wherein the time information refers to a duration from receiving the policy task to completing the policy task.
As an example, the historical processing duration of each processing object is obtained, then the average processing duration of each processing object is calculated, each task is correspondingly matched in the task processing set through the average processing duration, so that the matching degree of each processing object corresponding to each task is generated, and the target processing object with the preset screening proportion is selected through the matching degree and the preset screening proportion, so that the target object group is formed.
In a specific implementation, when the matching degree between the file processing object and the task is high, the processing object is determined to be the target processing object of the task.
The preset policy distribution model is generated from historical processing data as described in the step S140.
In one embodiment of the present invention, the specific process of "the preset policy allocation model is generated by historical processing data" in step S140 may be further described in conjunction with the following description.
As will be described in the following steps,
S610, acquiring historical processing data of historical processing personnel on the historical insurance policies, wherein the historical processing data comprises historical insurance policy amounts, historical problem rates and historical success rates corresponding to each historical insurance policy, and the historical success rates comprise renewal success rates and signing success rates;
S620, generating the preset policy allocation model through the historical policy amount, the historical problem rate and the historical success rate.
It should be noted that, the historical policy amount includes premium and payable amount; the historical problem rate refers to actual problems generated by historical processing personnel in the processing process; the historical success rate refers to the proportion of renewal success and ticket success in the total completed tasks; the renewal success is the proportion of the policy tasks which are successfully renewed in the process of processing the same policy task by the history processor in the total completed policy tasks; the successful signing is the proportion of the signed policy task to all the newly signed policy tasks successfully completed in the process of processing the newly signed policy task by the history processor.
As one example, a preset policy allocation model for enabling accurate positioning of insurance products is generated from a historical policy amount, a historical problem rate, and a historical success rate.
In a specific implementation, the historical processing data is obtained from a policy database, and the source data is firstly standardized to filter sensitive information after the historical processing data is obtained, so that the privacy of a client is ensured not to be revealed, and the standardized historical processing data is used for constructing a preset policy distribution model.
As described in the step S150, a target person of a target task is determined in the target object group.
In one embodiment of the present invention, the specific procedure of "determining the target person of the target task in the target object group" described in step S150 may be further described in conjunction with the following description.
As will be described in the following steps,
S710, determining whether each target object of the target object group is in an incumbent state;
s720, screening the target object group to obtain a first target object set in an incumbent state;
S730, acquiring task processing conditions of each target user in the first target object set, wherein the task processing conditions comprise task weights and task quantity;
s740, determining idle users in the first target object set according to the task weight and the task number;
s750, generating a second target object set according to the idle user;
s760, acquiring professional evaluation values of all the processors in the second target object set, wherein the professional evaluation values are obtained through the historical problem rate and the historical success rate of the processors;
S770, sorting each processor according to the values according to the professional evaluation values to generate an allocation priority table;
and S710, sequentially distributing the target tasks to the target persons corresponding to the high-low tasks in the distribution priority table.
It should be noted that, the on duty status is used to determine whether the target object is still on duty; the task processing condition is used for determining the current task completion condition or task load of the target object; the specialty evaluation value is used to determine the degree of specialty of the target object.
As an example, determining whether each target object is still incumbent in the target object group, excluding target objects which are not incumbent, and reserving the incumbent target objects; determining task processing conditions of all target users according to the incumbent target objects, and judging whether the target users are idle or not according to the task processing conditions; when the task load is greater than a preset task processing threshold, the target user is determined to be in a busy state at present; when the task load is smaller than a preset processing threshold, the target user is determined to be in a relatively idle state at present; screening target users in an idle state, acquiring professional evaluation values corresponding to the target users in the idle state, and sequencing from high to low to form an allocation priority table, and sequentially allocating corresponding report tasks to target personnel in the allocation priority table.
As an example, determining whether each target object is still incumbent in the target object group, excluding target objects which are not incumbent, and reserving the incumbent target objects; determining task processing conditions of all target users according to the incumbent target objects, and judging whether the target users are idle or not according to the task processing conditions; when the number of the current task completion reaches 80% of the total task amount, the target user is determined to be in a relatively idle state at present; when the number of the current task completion does not reach 20% of the total task amount, the target user is determined to be in a busy state at present; screening target users in an idle state, acquiring professional evaluation values corresponding to the target users in the idle state, and sequencing from high to low to form an allocation priority table, and sequentially allocating corresponding report tasks to target personnel in the allocation priority table.
In a specific implementation, the report tasks with higher priority are distributed to corresponding target personnel through a distribution priority table; and obtaining target personnel most suitable for processing the newspaper task through multiple screening.
For system embodiments, the description is relatively simple as it is substantially similar to method embodiments, and reference is made to the description of method embodiments for relevant points.
Referring to FIG. 2, a block diagram of an insurance task distribution system based on big data according to an embodiment of the present application is shown;
an insurance task distribution system based on big data, the system comprising:
a first generation module 210 for generating a set of policy tasks in response to a user request;
A second generating module 220, configured to generate a scheduled task list according to the policy task set and arranged according to a priority degradation order, where the scheduled task list includes a first task flow for indicating a priority duration and a second task flow for indicating a priority policy amount;
A third generating module 230, configured to generate, according to the first task flow and the second task flow, a task processing set with the same risk type according to the same risk type;
A first determining module 240, configured to determine a target object group in a preset policy allocation model according to the task processing set, where the preset policy allocation model is generated by historical processing data;
A second determining module 250 is configured to determine a target person of the target task in the target object group.
In an embodiment of the present invention, the second generating module 220 includes:
The decomposition sub-module is used for carrying out text analysis and decomposition on the policy task set to obtain a plurality of to-be-continued policy tasks and a plurality of newly signed policy tasks;
The first generation sub-module is used for determining the duration of each to-be-guaranteed policy task, and sequencing a plurality of to-be-guaranteed policy tasks from short to long according to the duration of the duration to generate a first task flow; the method comprises the steps of obtaining the amount of a policy of each new signing and ordering policy task, and ordering a plurality of new signing and ordering policy tasks from large to small according to the amount of the policy to generate a second task stream;
and the second generation sub-module is used for generating the scheduling task list according to the first task stream and the second task stream.
In an embodiment of the present invention, the third generating module 230 includes:
A dividing sub-module, configured to divide the first task flow and the second task flow into task processing blocks with different priorities, where the task processing blocks include a first priority processing task block, a second priority processing task block, a first post-processing task block and a second post-processing task block;
The third generation sub-module is used for acquiring each priority processing task with the same characteristic attribute according to the first priority processing task block and the second priority processing task block, and generating a priority task processing set through each priority processing task; acquiring each post-processing task with the same characteristic attribute according to the first post-processing task block and the second post-processing task block, and generating a post-processing task processing set through each post-processing task; wherein the characteristic attribute is an insurance type;
And the fourth generation sub-module is used for generating the task processing set according to the priority task processing set and the deferred task processing set.
In an embodiment of the present invention, the first determining module 240 includes:
The first acquisition sub-module is used for acquiring historical task information in the preset policy allocation model; the history task information at least comprises a plurality of history processing tasks and target dangerous types corresponding to the history processing tasks; each of the historical processing tasks corresponds to at least one processing object;
and the first determining submodule is used for determining a target object group according to the historical task information and the task processing set.
In an embodiment of the present invention, the first determining sub-module includes:
an obtaining unit, configured to obtain a history processing duration of each processing object in the history task information;
A first determining unit configured to determine an average processing duration of each of the processing objects according to the historical processing duration of each of the processing objects;
The second determining unit is used for determining the matching degree corresponding to each processing object according to the average processing duration and the completion cycle time corresponding to each task in the task processing set;
The third determining unit is used for determining a plurality of target processing objects in each processing object according to the matching degree and a preset screening proportion;
and the generating unit is used for generating the target object group according to a plurality of target processing objects.
In an embodiment of the present invention, the first determining module 240 includes:
the second acquisition sub-module is used for acquiring historical processing data of the historical policy by the historical processing personnel, wherein the historical processing data comprises a historical policy amount, a historical problem rate and a historical success rate corresponding to each historical policy, and the historical success rate comprises a renewal success rate and a ticket signing success rate;
and a fifth generation sub-module, configured to generate the preset policy allocation model according to the historical policy amount, the historical problem rate and the historical success rate.
In an embodiment of the present invention, the second determining module 250 includes:
A second determining sub-module for determining whether each target object of the target object group is in an incumbent state;
the screening sub-module is used for screening the target object group to obtain a first target object set in an incumbent state;
a third obtaining sub-module, configured to obtain task processing situations of each target user in the first target object set, where the task processing situations include task weights and task numbers;
A third determining submodule, configured to determine an idle user in the first target object set according to the task weight and the task number;
A sixth generation sub-module, configured to generate a second target object set according to the idle user;
A fourth obtaining sub-module, configured to obtain a professional evaluation value of each processor in the second target object set, where the professional evaluation value is obtained through a historical problem rate and a historical success rate of the processor;
the sorting sub-module is used for sorting each processor according to the professional evaluation value and the numerical value to generate an allocation priority table;
and the allocation submodule is used for sequentially allocating the target tasks to the target personnel corresponding to the high-low allocation priority table.
Referring to fig. 3, a computer device of the insurance task allocation method based on big data of the present invention may specifically include the following:
The computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor, or a local bus 18 using any of a variety of bus 18 architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus 18, micro channel architecture (MAC) bus 18, enhanced ISA bus 18, video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, the program modules 42 being configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, a memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in fig. 3, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, data backup storage systems 34, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing a big data based insurance task allocation method provided by an embodiment of the present invention.
That is, the processing unit 16 realizes when executing the program: generating a policy task set in response to a user request; generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; and determining target personnel of the target task in the target object group.
In an embodiment of the present application, the present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an insurance task allocation method based on big data as provided in all embodiments of the present application:
That is, the program is implemented when executed by a processor: generating a policy task set in response to a user request; generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; and determining target personnel of the target task in the target object group.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device that comprises the element.
The above detailed description of the insurance task allocation method and system based on big data provided by the application applies specific examples to illustrate the principle and implementation of the application, and the above examples are only used for helping to understand the method and core idea of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (5)
1. An insurance task allocation method based on big data, the method comprising:
generating a policy task set in response to a user request;
Generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow for representing priority renewal and a second task flow for representing priority policy money; the insurance policy task set is subjected to text analysis and decomposition into a plurality of to-be-continued insurance policy tasks and a plurality of newly signed insurance policy tasks; determining the renewal time of each to-be-guaranteed policy task, and sequencing a plurality of to-be-guaranteed policy tasks from short to long according to the length of the renewal time to generate a first task flow; the method comprises the steps of obtaining the amount of a policy of each new signing and ordering policy task, and ordering a plurality of new signing and ordering policy tasks from large to small according to the amount of the policy to generate a second task stream; generating the scheduling task list according to the first task stream and the second task stream; analyzing each policy task in the policy task set through text analysis, extracting key information, and decomposing the key information into a to-be-continued policy task and a newly signed policy task according to the key information; the key information comprises policy expiration time, primary signing user, gender, region and age;
Generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; dividing the first task flow and the second task flow into task processing blocks with different priorities, wherein the task processing blocks comprise a first priority processing task block, a second priority processing task block, a first post-processing task block and a second post-processing task block; acquiring each priority processing task with the same characteristic attribute from the first priority processing task block and the second priority processing task block, and generating a priority task processing set through each priority processing task; acquiring each post-processing task with the same characteristic attribute according to the first post-processing task block and the second post-processing task block, and generating a post-processing task processing set through each post-processing task; the characteristic attribute is an insurance type; generating the task processing set according to the priority task processing set and the deferred task processing set; screening out policy tasks with the same risk types from the first task stream and the second task stream, and dividing the policy tasks with the same risk types into task blocks with a plurality of processing attributes according to priority degrees and preset quantity limits, wherein the processing attributes are used for indicating the priority degrees of the corresponding task blocks, and the priority degrees comprise priority treatment and delay treatment;
Determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; acquiring historical processing data of historical processing personnel on the historical insurance policies, wherein the historical processing data comprises historical insurance policy amounts, historical problem rates and historical success rates corresponding to each historical insurance policy, and the historical success rates comprise renewal success rates and signing success rates; generating the preset policy allocation model through the historical policy amount, the historical problem rate and the historical success rate; acquiring historical task information from the preset policy allocation model; the history task information at least comprises a plurality of history processing tasks and target dangerous types corresponding to the history processing tasks; each of the historical processing tasks corresponds to at least one processing object; determining a target object group according to the historical task information and the task processing set; acquiring the historical processing time length of each processing object from the historical task information; determining the average processing duration of each processing object according to the historical processing duration of each processing object; determining the matching degree corresponding to each processing object according to the average processing duration and the completion cycle time corresponding to each task in the task processing set; determining a plurality of target processing objects in each processing object according to the matching degree and a preset screening proportion; generating the target object group according to a plurality of target processing objects;
and determining target personnel of the target task in the target object group.
2. The method of claim 1, wherein the step of determining a target person for a target task in the target object group comprises:
Determining whether each target object of the target object group is in an incumbent state;
screening the target object group to obtain a first target object set in an incumbent state;
acquiring task processing conditions of each target user in the first target object set, wherein the task processing conditions comprise task weights and task numbers;
Determining idle users in the first target object set according to the task weight and the task number;
generating a second target object set according to the idle user;
Acquiring professional evaluation values of all the processors in the second target object set, wherein the professional evaluation values are obtained through the historical problem rate and the historical success rate of the processors;
sorting each processor according to the values according to the professional evaluation value to generate an allocation priority table;
And sequentially distributing the target tasks to the target persons corresponding to the high-low tasks in the distribution priority table.
3. An insurance task distribution system based on big data, said system comprising:
the first generation module is used for responding to the user request to generate a policy task set;
the second generation module is used for generating a scheduling task list according to the policy task set and the priority degradation sequence, wherein the scheduling task list comprises a first task flow used for representing the priority renewal and a second task flow used for representing the priority policy amount; the insurance policy task set is subjected to text analysis and decomposition into a plurality of to-be-continued insurance policy tasks and a plurality of newly signed insurance policy tasks; determining the renewal time of each to-be-guaranteed policy task, and sequencing a plurality of to-be-guaranteed policy tasks from short to long according to the length of the renewal time to generate a first task flow; the method comprises the steps of obtaining the amount of a policy of each new signing and ordering policy task, and ordering a plurality of new signing and ordering policy tasks from large to small according to the amount of the policy to generate a second task stream; generating the scheduling task list according to the first task stream and the second task stream; analyzing each policy task in the policy task set through text analysis, extracting key information, and decomposing the key information into a to-be-continued policy task and a newly signed policy task according to the key information; the key information comprises one or more of policy expiration time, primary sign-up user, gender, region and age;
The third generation module is used for generating the same task processing set of each dangerous seed according to the first task stream and the second task stream according to the same dangerous seed; dividing the first task flow and the second task flow into task processing blocks with different priorities, wherein the task processing blocks comprise a first priority processing task block, a second priority processing task block, a first post-processing task block and a second post-processing task block; acquiring each priority processing task with the same characteristic attribute from the first priority processing task block and the second priority processing task block, and generating a priority task processing set through each priority processing task; acquiring each post-processing task with the same characteristic attribute according to the first post-processing task block and the second post-processing task block, and generating a post-processing task processing set through each post-processing task; the characteristic attribute is an insurance type; generating the task processing set according to the priority task processing set and the deferred task processing set; screening out policy tasks with the same risk types from the first task stream and the second task stream, and dividing the policy tasks with the same risk types into task blocks with a plurality of processing attributes according to priority degrees and preset quantity limits, wherein the processing attributes are used for indicating the priority degrees of the corresponding task blocks, and the priority degrees comprise priority treatment and delay treatment;
The first determining module is used for determining a target object group in a preset policy distribution model according to the task processing set, wherein the preset policy distribution model is generated by historical processing data; acquiring historical task information from the preset policy allocation model; the history task information at least comprises a plurality of history processing tasks and target dangerous types corresponding to the history processing tasks; each of the historical processing tasks corresponds to at least one processing object; determining a target object group according to the historical task information and the task processing set; acquiring the historical processing time length of each processing object from the historical task information; determining the average processing duration of each processing object according to the historical processing duration of each processing object; determining the matching degree corresponding to each processing object according to the average processing duration and the completion cycle time corresponding to each task in the task processing set; determining a plurality of target processing objects in each processing object according to the matching degree and a preset screening proportion; generating the target object group according to a plurality of target processing objects;
And the second determining module is used for determining target personnel of a target task in the target object group.
4. A computer device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which computer program, when executed by the processor, implements the method of any one of claims 1 to 2.
5. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1 to 2.
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| CN109872036A (en) * | 2019-01-10 | 2019-06-11 | 平安科技（深圳）有限公司 | Method for allocating tasks, device and computer equipment based on sorting algorithm |
| CN113467969A (en) * | 2021-06-22 | 2021-10-01 | 上海星融汽车科技有限公司 | Method for processing message accumulation |
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