CN114626735A - Urging case allocation method, urging case allocation device, urging case allocation equipment and computer readable storage medium - Google Patents

Urging case allocation method, urging case allocation device, urging case allocation equipment and computer readable storage medium Download PDF

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CN114626735A
CN114626735A CN202210289589.6A CN202210289589A CN114626735A CN 114626735 A CN114626735 A CN 114626735A CN 202210289589 A CN202210289589 A CN 202210289589A CN 114626735 A CN114626735 A CN 114626735A
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徐寅磊
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Abstract

The invention relates to an artificial intelligence technology, and discloses a method for distributing cases to be collected, which comprises the following steps: acquiring case information and a collection of collection urging members of all cases to be urged; classifying the collection cases by utilizing a pre-constructed random forest classification model according to case information to obtain the grades of the collection cases; the collection efficiency and the collection rate are integrated to obtain the collection efficiency of the collector on the same day; acquiring the performance of a receiver corresponding to the case to be received, and calculating the preprocessing time of the case to be received based on the performance of the receiver and the grade corresponding to the case to be received; and distributing the cases on the next day based on the level of the cases to be collected, the preprocessing time and the collection efficiency of the collectors on the same day. In addition, the invention also relates to a block chain technology, and the case information can be stored in the nodes of the block chain. The invention also provides a collection case distribution device, electronic equipment and a storage medium. The invention can improve the fairness of distribution of the collection cases.

Description

Urging case allocation method, urging case allocation device, urging case allocation equipment and computer readable storage medium
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method, a device and equipment for distributing an induced collection case and a computer readable storage medium.
Background
With the rapid development of economy in recent years, the consumption modes of people are more and more diversified, various modern financial services such as small credit, consumption amount, credit card business and the like are developed vigorously, the number of overdue cases is increased continuously, and the cases are generally required to be allocated to professional collectors for collection aiming at the overdue cases.
At present, commonly used case distribution modes are mainly that the case is evenly distributed according to the number of cases or only distributed according to collection amount, after the cases are distributed, collection staff communicate with customers in a telephone communication mode, a foreign visit mode and the like according to distributed case information, fair distribution of case collection difficulty is difficult to guarantee by the aid of the distribution modes, meanwhile, collection staff cannot timely make collection response, case processing time is long, unfair performance assessment of the collection staff is caused, and great resource waste is brought to platform cost.
In summary, the fairness of the current collection case allocation needs to be improved.
Disclosure of Invention
The invention provides a method, a device and equipment for allocating collection cases and a computer readable storage medium, and mainly aims to solve the problem of low precision in product recommendation.
In order to achieve the above object, the present invention provides a catalytic recovery case distribution method, comprising:
acquiring case information and a collection of collection urging members of all cases to be urged;
classifying the case to be hastened to be collected by utilizing a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be collected;
acquiring the daily workload of each acquirer hastening in the acquirer hastening set, calculating the daily hastening efficiency of each acquirer hastening, acquiring the daily hastening amount of each acquirer hastening in the acquirer hastening set, calculating the daily hastening amount rate of each acquirer, and integrating the hastening efficiency and the hastening amount rate to obtain the daily hastening work efficiency of the acquirer;
acquiring the performance of a receiver corresponding to the case to be collected, and calculating the preprocessing time of the case to be collected based on the performance of the receiver and the grade corresponding to the case to be collected;
and distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastener on the same day.
Optionally, the acquiring case information of all cases to be hastened includes:
receiving a case allocation request for receiving an invitation, inquiring a preset credit database, and acquiring a credit case with an overdue mark and the overdue time of the credit case;
taking all credit cases with the overdue time exceeding a preset time threshold value as all cases to be collected corresponding to the case distribution request to be collected;
and acquiring the case information of all cases with collection urging from the preset credit database, wherein the case information comprises collection urging limits, collection urging times, expiration time, corresponding collectors and client credit scores corresponding to the cases to be collected.
Optionally, the classifying the case to be hastened to be collected by using a pre-constructed random forest classification model according to the case information to obtain the level of the case to be hastened to be collected includes:
performing word segmentation and quantization processing on the case information to obtain a case information text vector set;
obtaining all decision trees in the random forest classification model and decision dimension indexes and decision conditions of at least one layer of nodes in each decision tree;
according to the decision dimension index of a first node in the random forest classification model, performing feature extraction on the case information text vector set to obtain a feature value of the case information text vector set on the splitting dimension of the first node;
judging the characteristic value according to the decision condition of the first node, and determining a traversed second node from the branch nodes of the first node according to the judgment result;
and according to the current decision dimension index and decision conditions, continuously extracting the characteristic value of the case information text vector set at the second node and determining the next node to be traversed until the traversal of the decision tree is completed to obtain the level of the case to be urged to be collected.
Optionally, the obtaining the daily workload of each acquirer hasten in the acquirer collection and calculating the daily hasten efficiency of each acquirer includes:
acquiring the total number of cases to be processed in the current day from the preset credit database at preset time, and counting the workload of each acquirer in the acquirer collection in the current day;
and calculating the daily collection efficiency of each collector by using the total number of the cases to be processed on the day and the workload of each collector.
Optionally, the calculating the preprocessing time of the case to be urged to be received based on the performance of the urging member and the corresponding level of the case to be urged to be received includes:
acquiring the grade corresponding to the case to be urged to be received, and counting the first preprocessing time of the grade urging to be received;
extracting the average processing time required by a case of the performance of the receiver to obtain second preprocessing time;
and carrying out weighted average on the first pretreatment time and the second pretreatment time to obtain the pretreatment time of the case to be catalytically collected.
Optionally, the allocating the next day case based on the level of the case to be hastened, the preprocessing time, and the hastened work efficiency of the hastened collector on the same day includes:
acquiring cases to be processed on the next day from the preset credit database, and sequencing the cases to be processed on the next day based on the level of the cases to be hastened to be received and the preprocessing time to obtain a case queue for hastened to be received;
and distributing the case queue to be hastened according to the proportion of the work efficiency of hastened collection of the hastener on the same day.
Optionally, before the case to be hastened to be collected is classified by using a pre-constructed random forest classification model according to the case information and the level of the case to be hastened to be collected is obtained, the method further includes:
respectively carrying out word segmentation quantification processing on the collection amount, the collection times, the overdue time and the client credit score in the case information to obtain a collection amount text vector set, a collection times text vector set, an overdue time text vector set and a client credit score text vector set;
selecting one of the text vector of the charge amount, the text vector of the charge time, the text vector of the overdue time and the text vector of the credit score of the client one by one from the text vector set of the charge amount, the text vector set of the charge time, the text vector of the overdue time and the text vector of the credit score of the client respectively as a target text vector of the charge amount, a target text vector of the charge time, a target text vector of the overdue time and a target text vector of the credit score of the client;
assigning a preset decision function by taking the target charge amount text vector as a parameter, and generating a charge amount decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target number of induced receipts text vector as a parameter, and generating an induced collection decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target overdue time text vector as a parameter, and generating an overdue time decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target client credit scoring text vector as a parameter, and generating a client credit scoring decision tree by taking the assigned decision function as a decision condition;
and summarizing the decision tree for urging income limit, the decision tree for urging income times, the decision tree for overdue time and the decision tree for client credit scoring to obtain the random forest classification model.
In order to solve the above problems, the present invention also provides a catalytic recovery case dispensing device, comprising:
the acquisition module of the cases to be urged to be collected is used for acquiring the case information and the collection of urging members of all the cases to be urged to be collected;
the grading module is used for grading the case to be hastened to be received by utilizing a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be received;
the collection efficiency generation module is used for acquiring the workload of each collector in the collection of collectors, calculating the collection efficiency of each collector in the same day, acquiring the collection amount of each collector in the collection of collectors, calculating the collection amount rate of each collector in the same day, and integrating the collection efficiency and the collection amount rate to obtain the collection efficiency of the collector in the same day;
the pretreatment time calculation module is used for acquiring the performance of a receiver corresponding to the case to be subjected to forced collection and calculating the pretreatment time of the case to be subjected to forced collection based on the performance of the receiver and the grade corresponding to the case to be subjected to forced collection;
and the distribution module is used for distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastened collector on the same day.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the catalytic case allocation method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the catalytic recovery case distribution method.
In the embodiment of the invention, the case to be hastened to be collected is graded by utilizing the pre-constructed random forest classification model to obtain the grade of the case to be hastened to be collected, and the random forest classification model is utilized to be beneficial to improving the grading accuracy of the case to be hastened to be collected; acquiring the workload of each acquirer hastening in the acquirer collection on the same day, calculating the acquiring efficiency of each acquirer hastening on the same day, acquiring the amount of the each acquirer hastening in the acquirer collection on the same day, calculating the rate of the amount of the each acquirer hastening on the same day, and integrating the efficiency of the hastening and the rate of the amount of the hastening to obtain the efficiency of the acquirer hastening on the same day, and integrating the workload and the amount to obtain the efficiency of the acquirer hastening on the same day, so that the efficiency of the acquirer hastening is more accurate; calculating the preprocessing time of the case to be urged to be retracted based on the performance of the urging member and the grade corresponding to the case to be urged to be retracted, so that the preprocessing time of the case to be urged to be retracted is more accurate; based on wait to urge the level of receipts case the preprocessing time urge receipts work efficiency distribution second day case of receipts person on the day, the difficulty degree of expecting receipts case is taken into account comprehensively, urge receipts person's work efficiency and urge the condition of receipts case execution every day, carry out dynamic allocation to the second day case, promoted wait to urge the fairness of receipts case distribution. Therefore, the method, the device, the equipment and the computer readable storage medium for distributing the collection cases can solve the problem that the fairness of the distribution of the current collection cases is low.
Drawings
FIG. 1 is a schematic flow chart illustrating a catalytic recovery case distribution method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of case information acquisition according to an embodiment of the present invention; (ii) a
Fig. 3 is a schematic flow chart of constructing a random forest classification model according to an embodiment of the present invention; FIG. 4 is a schematic flow chart illustrating a case grading process for an incoming case according to an embodiment of the present invention;
fig. 5 is a schematic flow chart illustrating the calculation of the daily collection efficiency of the collector according to an embodiment of the present invention;
fig. 6 is a schematic flow chart illustrating calculation of the daily collection rate of the collector according to an embodiment of the present invention;
FIG. 7 is a schematic flowchart illustrating a process of calculating the pre-processing time of a case to be catalyzed according to an embodiment of the present invention;
FIG. 8 is a schematic flow chart illustrating the distribution of cases to be hastened according to an embodiment of the present invention;
FIG. 9 is a functional block diagram of a catalytic case distribution apparatus according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of an electronic device for implementing the method for distributing cases to be urged to receive according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a distribution method of collection cases. The main body of the to-be-hastened case allocation method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the to-be-hastened case allocation method may be executed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart illustrating a catalytic recovery case distribution method according to an embodiment of the present invention. In this embodiment, the distribution method for the to-be-hasten cases includes the following steps S1-S5:
and S1, acquiring case information and a collection of cases to be collected.
In the embodiment of the invention, the case to be catalyzed can be one or more pieces.
In detail, referring to FIG. 2, the S1 includes the following steps S11-S13:
s11, receiving a case allocation request for collection, inquiring a preset credit database, and acquiring a credit case with an overdue mark and the overdue time of the credit case;
s12, taking all credit cases of which the overdue time exceeds a preset time threshold value as all cases to be collected corresponding to the case distribution request to be collected;
s13, obtaining the case information of all cases with the collection urging from the preset credit database, wherein the case information comprises collection urging limits, collection urging times, overdue time, corresponding collectors and client credit scores corresponding to the cases to be collected.
The triggering form of the case allocation request to be hastened is not limited in the embodiment of the invention, and the case allocation request can be triggered by clicking an 'allocation' button on a terminal by a user, or the allocation can be triggered according to preset time, or the allocation can be triggered according to the preset number of cases to be hastened.
In the embodiment of the invention, the preset credit database refers to a database preset on a preset credit platform, credit cases of different users are recorded in the preset credit database, and the credit cases are associated with information such as borrowing date and repayment date.
In the embodiment of the present invention, the preset time threshold refers to a preset credit expiration time threshold, and the threshold may be set according to a specific scenario, for example, the preset time threshold may be set to 10 days.
In the embodiment of the invention, overdue credit cases in the preset credit platform can be automatically acquired, and the collection urging cases needing case distribution are selected from the overdue credit cases, so that the collection urging case distribution is more timely.
S2, classifying the cases to be urged to be received by utilizing a pre-constructed random forest classification model according to the case information to obtain the grades of the cases to be urged to be received.
In the embodiment of the invention, the Random Forest classification model (RF for short) is an algorithm for integrating a plurality of trees by the idea of ensemble learning, and the basic unit of the algorithm is a decision tree. Taking the classification problem as an example, each decision tree is a classifier, for an input sample, N classification results are obtained for N trees, all classification voting results are integrated in a random forest, and the class with the largest voting frequency is designated as the final output, so that the optimal class is obtained.
In one embodiment of the present invention, referring to fig. 3, before the classification of the case to be induced by using the pre-constructed random forest classification model in S2, the method further includes the following steps S201 to S207:
s201, performing word segmentation quantification processing on the collection amount, the collection times, the overdue time and the client credit score in the case information respectively to obtain a collection amount text vector set, a collection times text vector set, an overdue time text vector set and a client credit score text vector set;
s202, selecting one of the text vector of the collection amount, the text vector of the collection times, the text vector of the overdue time and the text vector of the credit rating of the client one by one from the text vector set of the collection amount, the text vector set of the collection times, the text vector of the overdue time and the text vector of the credit rating of the client as a target text vector of the collection amount, a target text vector of the collection times, a target text vector of the overdue time and a target text vector of the credit rating of the client;
s203, assigning a preset decision function by taking the target income promoting limit text vector as a parameter, and generating an income promoting limit decision tree by taking the assigned decision function as a decision condition;
s204, assigning a preset decision function by taking the target number of induced receipts text vector as a parameter, and generating an induced receipts decision tree by taking the assigned decision function as a decision condition;
s205, assigning a preset decision function by taking the target overdue time text vector as a parameter, and generating an overdue time decision tree by taking the assigned decision function as a decision condition;
s206, assigning a preset decision function by taking the target client credit scoring text vector as a parameter, and generating a client credit scoring decision tree by taking the assigned decision function as a decision condition;
and S207, summarizing the decision tree for the collection amount, the decision tree for the collection times, the decision tree for the overdue time and the decision tree for the credit scoring of the client to obtain the random forest classification model.
Illustratively, the decision function may be:
Figure BDA0003561155970000081
wherein f (x) is an output value of the decision function, x is a parameter of the decision function, and g (y) is an input value of the decision function.
In detail, in the embodiment of the present invention, one of the collection-promoting quota text vectors may be selected one by one from the collection of collection-promoting quota text vectors as a target collection-promoting quota text vector, the target collection-promoting quota text vector is used to assign a value to the parameter x of the decision function, and the assigned decision function is used as a decision condition to generate the following collection-promoting quota decision tree:
when the input value g (y) of the charge amount prompting decision tree is the same as the parameter x of the charge amount prompting decision tree, the output value f (x) of the charge amount prompting decision tree is alpha;
when the input g (y) of the charge amount decision tree is different from the parameter x of the charge amount decision tree, the output value f (x) of the charge amount decision tree is beta.
In the embodiment of the present invention, the generation methods of the collection number decision tree, the overdue time decision tree and the client credit scoring decision tree are the same as those of the collection amount decision tree, and thus are not described herein again.
Further, referring to fig. 4, the S2 includes the following steps S211 to S215:
s211, performing word segmentation and quantization processing on the case information to obtain a case information text vector set;
s212, obtaining decision dimension indexes and decision conditions of all decision trees in the random forest classification model and at least one layer of nodes in each decision tree;
s213, according to the decision dimension index of the first node in the random forest classification model, performing feature extraction on the case information text vector set to obtain a feature value of the case information text vector set on the splitting dimension of the first node;
s214, judging the characteristic value according to the decision condition of the first node, and determining a traversed second node from the branch nodes of the first node according to a judgment result;
s215, according to the current decision dimension index and the decision condition, continuously extracting the characteristic value of the case information text vector set at the second node and determining the next node to be traversed until the traversal of the decision tree is completed, so as to obtain the level of the case to be urged to be collected.
In the embodiment of the present invention, the decision dimension index is used to uniquely determine a split dimension, and the split dimension and the decision condition are used to determine a next node to be traversed from a branch node of a corresponding node.
In the embodiment of the invention, the case information is utilized to grade the emergency and importance degree of the case to be collected, such as: will wait to urge the receipts case to divide into one-level, second grade, tertiary, level four, five, be convenient for according to the rank will wait to urge the receipts case to distribute to the blind person who urges.
S3, acquiring the workload of each current collector in the current collector collection, calculating the current collection efficiency of each current collector, acquiring the current collection amount of each current collector in the current collector collection, calculating the current collection amount rate of each current collector, and integrating the current collection efficiency and the collection amount rate to obtain the current collection efficiency of the current collector.
In detail, referring to fig. 5, the step of acquiring the daily workload of each acquirer in the acquirer hastening set and calculating the daily efficiency of the acquirer in S3 includes the following steps S301-S302:
s301, acquiring the total number of cases to be processed in the current day from the preset credit database at preset time, and counting the workload of each acquirer in the acquirer collection in the current day;
s302, calculating the daily collection efficiency of each collector by using the total number of the cases to be processed on the same day and the workload of each collector.
Further, referring to fig. 6, in step S3, obtaining the daily amount of the induced collection of each induced collector in the induced collector set, and calculating the daily amount rate of the induced collection of each induced collector includes the following steps S311 to S312:
s311, acquiring the total amount of the cases to be processed in the current day from the preset credit database at preset time, and counting the current amount of each acquirer in the acquirer hasten;
and S312, calculating the daily collection efficiency of each collector by using the total amount of the cases to be processed on the same day and the daily collection amount of each collector.
In the embodiment of the invention, the collection efficiency and the collection rate are weighted to obtain the current collection efficiency of the collector.
S4, acquiring the performance of the collector corresponding to the case to be collected, and calculating the preprocessing time of the case to be collected based on the performance of the collector and the grade corresponding to the case to be collected.
In the embodiment of the invention, the performance of the acquirer is the average performance of the acquirer within a certain time range, namely the comprehensive average value of the number of cases, the money amount and the dialing times of the acquirer within a certain time range.
In detail, referring to fig. 7, in S4, calculating the preprocessing time of the case to be urged to be collected based on the performance of the urging member and the corresponding level of the case to be urged to be collected includes the following steps S41-S43:
s41, obtaining the corresponding level of the case to be urged to be collected, and counting the first preprocessing time of the case to be urged to be collected;
s42, extracting the average processing time required by a case of the performance of the acquirer to obtain second preprocessing time;
s43, carrying out weighted average on the first pretreatment time and the second pretreatment time to obtain the pretreatment time of the case to be catalyzed.
In the embodiment of the invention, the first pretreatment time is obtained according to the grade of the case to be urged to be received, the second pretreatment time is obtained according to the performance of the urging member, the first pretreatment time and the second pretreatment time are integrated to obtain the pretreatment time of the case to be urged to be received, and the pretreatment time of the case to be urged to be received is more accurate by utilizing the grade of the case to be urged to be received and the performance of the urging member.
S5, distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastener on the same day.
In detail, referring to FIG. 8, the step S5 includes the following steps S51-S52:
s51, obtaining cases to be processed on the next day from the preset credit database, and sorting the cases to be processed on the next day based on the level of the cases to be hastened to be received and the preprocessing time to obtain a case queue for hastened to be received;
and S52, distributing the case queue to be hastened for receiving according to the proportion of the work efficiency of the hastened receiver on the same day.
In the embodiment of the invention, the cases to be processed on the next day are sorted by combining the grades of the cases to be hastened to be collected and the pretreatment time, so as to obtain a case queue for hastened to be collected; according to urge the proportion of the work efficiency of receiving of receipts person on the day, right wait to urge to receive the case and queue and distribute, real-time update every day urges to receive and queues and calculates urge the work efficiency of receiving of receipts person on the day distributes the case queue of receiving of the second day, and the easy degree of the case of urging to receive is taken into account comprehensively, urges the work efficiency of receipts person and the condition that the case of urging to receive was carried out every day, carries out dynamic distribution to the case of the second day, has promoted wait to urge the fairness of receiving the case distribution.
In the embodiment of the invention, the case to be hastened to be collected is graded by utilizing the pre-constructed random forest classification model to obtain the grade of the case to be hastened to be collected, and the random forest classification model is utilized to be beneficial to improving the grading accuracy of the case to be hastened to be collected; acquiring the workload of each acquirer hastening in the acquirer collection on the same day, calculating the acquiring efficiency of each acquirer hastening on the same day, acquiring the amount of the each acquirer hastening in the acquirer collection on the same day, calculating the rate of the amount of the each acquirer hastening on the same day, and integrating the efficiency of the hastening and the rate of the amount of the hastening to obtain the efficiency of the acquirer hastening on the same day, and integrating the workload and the amount to obtain the efficiency of the acquirer hastening on the same day, so that the efficiency of the acquirer hastening is more accurate; calculating the preprocessing time of the case to be urged to be retracted based on the performance of the urging member and the grade corresponding to the case to be urged to be retracted, so that the preprocessing time of the case to be urged to be retracted is more accurate; based on wait to urge the level of receipts case the preprocessing time urge receipts work efficiency distribution second day case of receipts person on the day, the difficulty degree of expecting receipts case is taken into account comprehensively, urge receipts person's work efficiency and urge the condition of receipts case execution every day, carry out dynamic allocation to the second day case, promoted wait to urge the fairness of receipts case distribution. Therefore, the method for distributing the collection cases can solve the problem of low fairness of the distribution of the current collection cases.
Fig. 9 is a functional block diagram of a catalytic case distribution device according to an embodiment of the present invention.
The to-be-collected case distribution device 100 of the present invention may be installed in an electronic device. According to the realized function, the distribution device 100 for the case to be urged to be collected may include a case to be urged to be collected acquisition module 101, a grading module 102, an urging work efficiency generation module 103, a preprocessing time calculation module 104 and a distribution module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the to-be-urged-to-be urged;
the grading module 102 is configured to grade the case to be hastened to be collected by using a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be collected;
the collection prompting work efficiency generation module 103 is configured to obtain the daily work load of each collector in the collection of collectors, calculate the daily collection prompting efficiency of each collector, obtain the daily collection amount of each collector in the collection of collectors, calculate the daily collection amount rate of each collector, and synthesize the collection prompting efficiency and the collection amount rate to obtain the daily collection prompting work efficiency of the collector;
the preprocessing time calculation module 104 is configured to obtain performance of a receiver corresponding to the case to be hastened, and calculate the preprocessing time of the case to be hastened based on the performance of the receiver and the level corresponding to the case to be hastened;
the distribution module 105 is used for distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastened collector on the same day.
In detail, when the modules in the distribution device 100 for cases to be induced to collect in the embodiment of the present invention are used, the same technical means as the induced-collection case distribution method described in fig. 1 to 8 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 10 is a schematic structural diagram of an electronic device for implementing an entry induction case distribution method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a hastelling case distribution program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a collection case allocation program) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, and the like. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various data, such as codes of a collection program, but also temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 10 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 10 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The hasty case allocation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize:
acquiring case information and a collection of collection urging members of all cases to be urged;
classifying the case to be hastened to be collected by utilizing a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be collected;
acquiring the daily workload of each acquirer hastening in the acquirer hastening set, calculating the daily hastening efficiency of each acquirer hastening, acquiring the daily hastening amount of each acquirer hastening in the acquirer hastening set, calculating the daily hastening amount rate of each acquirer, and integrating the hastening efficiency and the hastening amount rate to obtain the daily hastening work efficiency of the acquirer;
acquiring the performance of a receiver corresponding to the case to be collected, and calculating the preprocessing time of the case to be collected based on the performance of the receiver and the grade corresponding to the case to be collected;
distributing the cases on the next day based on the level of the cases to be urged to be received, the preprocessing time and the work efficiency of urging to be received on the same day by the urging member.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring case information and a collection of collection urging members of all cases to be urged to be collected;
classifying the cases to be urged to be received by utilizing a pre-constructed random forest classification model according to the case information to obtain the grades of the cases to be urged to be received;
acquiring the daily workload of each acquirer hastening in the acquirer hastening set, calculating the daily hastening efficiency of each acquirer hastening, acquiring the daily hastening amount of each acquirer hastening in the acquirer hastening set, calculating the daily hastening amount rate of each acquirer, and integrating the hastening efficiency and the hastening amount rate to obtain the daily hastening work efficiency of the acquirer;
acquiring the performance of a receiver corresponding to the case to be collected, and calculating the preprocessing time of the case to be collected based on the performance of the receiver and the grade corresponding to the case to be collected;
and distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastener on the same day.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. A collection case distribution method, comprising:
acquiring case information and a collection of collection urging members of all cases to be urged;
classifying the case to be hastened to be collected by utilizing a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be collected;
acquiring the daily workload of each acquirer hastening in the acquirer hastening set, calculating the daily hastening efficiency of each acquirer hastening, acquiring the daily hastening amount of each acquirer hastening in the acquirer hastening set, calculating the daily hastening amount rate of each acquirer, and integrating the hastening efficiency and the hastening amount rate to obtain the daily hastening work efficiency of the acquirer;
acquiring the performance of a receiver corresponding to the case to be collected, and calculating the preprocessing time of the case to be collected based on the performance of the receiver and the grade corresponding to the case to be collected;
and distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastener on the same day.
2. The collection case distribution method as claimed in claim 1, wherein the obtaining of the case information of all cases to be collected comprises:
receiving a case allocation request for receiving an invitation, inquiring a preset credit database, and acquiring a credit case with an overdue mark and the overdue time of the credit case;
taking all credit cases with the overdue time exceeding a preset time threshold value as all cases to be collected corresponding to the case distribution request to be collected;
and acquiring the case information of all cases with collection urging from the preset credit database, wherein the case information comprises collection urging limits, collection urging times, expiration time, corresponding collectors and client credit scores corresponding to the cases to be collected.
3. The case allocation method for hastening harvesting of claim 1, wherein the step of grading the case to be hastened harvesting by using a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened harvesting comprises the following steps:
performing word segmentation and quantization processing on the case information to obtain a case information text vector set;
obtaining all decision trees in the random forest classification model and decision dimension indexes and decision conditions of at least one layer of nodes in each decision tree;
according to the decision dimension index of a first node in the random forest classification model, performing feature extraction on the case information text vector set to obtain a feature value of the case information text vector set on the splitting dimension of the first node;
judging the characteristic value according to the decision condition of the first node, and determining a traversed second node from the branch nodes of the first node according to the judgment result;
and according to the current decision dimension index and decision conditions, continuously extracting the characteristic value of the case information text vector set at the second node and determining the next node to be traversed until the traversal of the decision tree is completed to obtain the level of the case to be urged to be collected.
4. The method for assigning hastally cases according to claim 1, wherein said obtaining the daily workload of each hastally member in said collection of hastally members and calculating the daily hastally efficiency of each hastally member comprises:
acquiring the total number of cases to be processed in the current day from the preset credit database at preset time, and counting the workload of each acquirer in the acquirer collection in the current day;
and calculating the daily collection efficiency of each collector by using the total number of the cases to be processed on the same day and the daily workload of each collector.
5. The collection case allocation method of claim 1, wherein the calculating the preprocessing time of the case to be collected based on the performance of the collector and the corresponding level of the case to be collected comprises:
acquiring the level corresponding to the case to be urged to be collected, and counting the first preprocessing time of the case to be urged to be collected;
extracting the average processing time required by a case of the performance of the receiver urging to obtain second preprocessing time;
and carrying out weighted average on the first pretreatment time and the second pretreatment time to obtain the pretreatment time of the case to be catalytically collected.
6. The collection case allocation method according to any one of claims 1 to 5, wherein the allocating cases on the next day based on the level of the case to be collected, the preprocessing time, and the collection work efficiency of the collector on the same day comprises:
acquiring cases to be processed on the next day from the preset credit database, and sequencing the cases to be processed on the next day based on the level of the cases to be hastened to be received and the preprocessing time to obtain a case queue for hastened to be received;
and distributing the case queue to be hastened according to the proportion of the work efficiency of hastened collection of the hastener on the same day.
7. The case allocation method for hastening harvesting of claim 1, wherein the method further comprises, before the case information is used for grading the case to be hastened harvested by using a pre-constructed random forest classification model to obtain the grade of the case to be hastened harvested:
respectively carrying out word segmentation quantification processing on the collection amount, the collection times, the overdue time and the client credit score in the case information to obtain a collection amount text vector set, a collection times text vector set, an overdue time text vector set and a client credit score text vector set;
selecting one of the text vector of the charge amount, the text vector of the charge time, the text vector of the overdue time and the text vector of the credit score of the client one by one from the text vector set of the charge amount, the text vector set of the charge time, the text vector of the overdue time and the text vector of the credit score of the client respectively as a target text vector of the charge amount, a target text vector of the charge time, a target text vector of the overdue time and a target text vector of the credit score of the client;
assigning a preset decision function by taking the target charge amount text vector as a parameter, and generating a charge amount decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target number of induced receipts text vector as a parameter, and generating an induced collection decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target overdue time text vector as a parameter, and generating an overdue time decision tree by taking the assigned decision function as a decision condition;
assigning a preset decision function by taking the target client credit scoring text vector as a parameter, and generating a client credit scoring decision tree by taking the assigned decision function as a decision condition;
and summarizing the decision tree for collection amount, the decision tree for collection times, the decision tree for overdue time and the decision tree for customer credit scoring to obtain the random forest classification model.
8. A catalytic case dispensing apparatus, the apparatus comprising:
the to-be-urged case acquisition module is used for acquiring case information and an urging member set of all cases to be urged;
the grading module is used for grading the case to be hastened to be received by utilizing a pre-constructed random forest classification model according to the case information to obtain the grade of the case to be hastened to be received;
the collection efficiency generation module is used for acquiring the workload of each collector in the collection of collectors, calculating the collection efficiency of each collector in the same day, acquiring the collection amount of each collector in the collection of collectors, calculating the collection amount rate of each collector in the same day, and integrating the collection efficiency and the collection amount rate to obtain the collection efficiency of the collector in the same day;
the pretreatment time calculation module is used for acquiring the performance of a receiver corresponding to the case to be subjected to forced collection and calculating the pretreatment time of the case to be subjected to forced collection based on the performance of the receiver and the grade corresponding to the case to be subjected to forced collection;
and the distribution module is used for distributing the cases on the next day based on the level of the cases to be hastened, the preprocessing time and the hastened work efficiency of the hastened collector on the same day.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of assigning receipts-forcing cases according to any one of claims 1 to 7.
CN202210289589.6A 2022-03-23 2022-03-23 Urging case allocation method, urging case allocation device, urging case allocation equipment and computer readable storage medium Pending CN114626735A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116341879A (en) * 2023-05-26 2023-06-27 杭州度言软件有限公司 Overdue asset collection intelligent case division method and system
CN117036010A (en) * 2023-10-10 2023-11-10 中信消费金融有限公司 Method and device for processing collection-urging case, server and readable storage medium
CN116341879B (en) * 2023-05-26 2024-05-31 杭州度言软件有限公司 Overdue asset collection intelligent case division method and system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116341879A (en) * 2023-05-26 2023-06-27 杭州度言软件有限公司 Overdue asset collection intelligent case division method and system
CN116341879B (en) * 2023-05-26 2024-05-31 杭州度言软件有限公司 Overdue asset collection intelligent case division method and system
CN117036010A (en) * 2023-10-10 2023-11-10 中信消费金融有限公司 Method and device for processing collection-urging case, server and readable storage medium

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