CN113822517A - Case division method and device based on capability matching - Google Patents
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Abstract
The invention discloses a case division method and device based on personnel ability matching, belongs to the technical field of case division, and aims to solve the technical problem of how to divide cases based on personnel ability matching. The method comprises the following steps: constructing a case division rule base for setting data related to capability evaluation, case classification and case allocation; constructing a round case rule base for setting and checking the relationship attribute between the related entities of the branch case; performing capability assessment on the person from the multiple dimensional assessments; constructing a heterogeneous association model between cases and people; for newly input cases, calculating case entity feature vectors based on case similarity division results; predicting the workload parameters of the case by adopting multilayer linear regression; and allocating the case to the matched personnel based on the case division rule base, the round case rule base, the capability evaluation result of each personnel, the heterogeneous association model and the workload parameters of the case.
Description
Technical Field
The invention relates to the technical field of case division, in particular to a case division method and device based on capability matching.
Background
The problems existing in the existing case division mechanism are increasingly obvious, because different cases have great difference in the aspects of type, difficulty, field and the like, the capacity requirements on case handling personnel are different, the problem that the optimal matching of the cases is difficult to realize is solved aiming at the defect that the existing business unified platform system lacks a method for quantitatively evaluating the workload of the cases and the workload of the case handling personnel, the existing case division mechanism is that cases are randomly divided mainly and divided secondarily, the system randomly distributes the cases according to a preset workflow, the optimal analysis of case matching is lacked, the personnel cannot interactively feed back with the system, and meanwhile, the system cannot find potential capacity characteristics for new people without case handling history.
Based on the above, how to divide cases based on matching is a technical problem to be solved.
Disclosure of Invention
The technical task of the invention is to provide a case division method and device based on capability matching to solve the problem of how to divide cases based on personnel capability matching.
The invention provides a case division method based on capability matching, which comprises the following steps:
constructing a case division rule base, wherein the case division rule base is used for setting data related to personnel capability evaluation, case classification and case distribution;
constructing a round case rule base, wherein the round case rule base is used for setting the relationship attributes among related entities of the inspection branch case, and distributing cases and personnel based on the relationship attributes;
based on a case division criterion library, performing capability evaluation on the personnel from multiple dimensional evaluations to obtain a capability evaluation result of each personnel;
constructing a heterogeneous association model between cases and persons, wherein the heterogeneous association model is used for calculating association weights among the cases, among the persons and between the cases and the persons, and realizing distribution among the cases and the persons based on the association weights;
for newly input cases, calculating case entity feature vectors based on case similarity division results;
the case workload evaluation method based on the deep case representation comprises the steps of carrying out data fusion on case entity feature vectors, converting the case entity feature vectors into a new data space, forming a deep fusion entity corresponding to a case, predicting the workload parameters of the case by adopting multilayer linear regression based on the deep fusion, and judging the difficulty and the complexity of the case based on the workload parameters;
and allocating the case to the matched personnel based on the case division rule base, the round case rule base, the capability evaluation result of each personnel, the heterogeneous association model and the workload parameters of the case.
As a preference, the first and second liquid crystal compositions are,
the case division rule base is provided with data standards, and the data standards comprise performance assessment metadata, case association metadata, case feedback metadata, personnel metadata, case classification metadata and historical case metadata;
based on the data standard, a business model is formed, wherein the business model comprises an access integration and visualization business model, a personnel business evaluation business model, a case grouping business model and a case undertaking business model, the access integration and visualization business model is used for setting and displaying related data, the personnel business evaluation business model is used for setting data related to personnel capacity evaluation, the case grouping business model is used for setting data related to case classification, and the case undertaking business model is used for setting data related to case distribution.
As a preference, the first and second liquid crystal compositions are,
the related entities for examination and case division comprise case entities, personnel entities and organization entity entities;
the case entity comprises regions and categories;
the human entities include name, gender, and age;
the organization comprises a name and a service type;
the relationship attributes among the related entities of the inspection and case sharing comprise case relationships, organization relationships and business relationships;
the case relation comprises a case type which is good for personnel to handle and a case type which is handled by personnel;
the organization relation comprises an organization mechanism to which the personnel belong and personnel positions;
the business relation comprises the business of the personnel.
As a preference, the first and second liquid crystal compositions are,
the rotation case rule base is used for configuring rule definitions, and the rule definitions comprise:
data normalization representation and business rule extraction;
rule execution, rule matching, rule priority, and conflict resolution;
the rules include defining rules by role, and defining rules by organizational personnel.
As a preference, the first and second liquid crystal compositions are,
evaluating the capability of personnel from five dimensions of service capability, case handling quality, active case handling and inspection and maintenance evaluation;
the business capability comprises a scholarly calendar, a scholarly position and a title;
the case handling capacity comprises case handling data and case type number;
the case handling quality comprises the consistency of the judgment result and the initiation of the complaint and the judgment;
the active case handling comprises difficult case tracing, correcting and anti-complaint;
the detection evaluation comprises a default condition, evaluation and feedback.
As a preference, the first and second liquid crystal compositions are,
calculating the case entity feature vector based on the case similarity division result, comprising the following steps:
dividing cases according to regions, fields, numbers of people, types of cases, difficulty and easiness of cases and coefficients, and calculating similarity coefficients in case groups;
dividing the cases according to case grouping conditions and case handling personnel, and calculating a similarity coefficient in a case handling group;
and extracting a case entity information model based on a time sequence mode, and calculating the characteristic vector of each type of case based on the similarity coefficient.
In a second aspect, the present invention provides an apparatus comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is configured to invoke the machine-readable program to perform the method of any of the first aspects.
In a third aspect, the present invention provides a computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of the first aspects.
The case division method and device based on the matching of case and capability of personnel checking and handling cases have the following advantages:
1. the existing scheme division mechanism of 'randomly dividing schemes mainly and appointing schemes as accessories' is avoided, the optimal analysis of human scheme matching is realized, and the scheme handling efficiency is improved;
2. discovering potential ability characteristics of new personnel without case handling history;
3. through constructing a heterogeneous network and carrying out unsupervised clustering analysis, potential association factors among persons, cases and cases are found, the optimal case matching relationship is obtained, and the optimal case matching problem is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block flow diagram of a case-splitting method based on capability matching according to example 1;
FIG. 2 is a diagram showing the factors for multidimensional evaluation of the ability of a examiner in the case division method based on ability matching in example 1;
FIG. 3 is a block diagram of the flow of calculating case feature vectors in the case-based capacity matching case-based case-splitting method of embodiment 1;
FIG. 4 is a block diagram of the deep case characterization extraction flow in the case-splitting method based on capability matching in example 1.
Detailed Description
The present invention is further described in the following with reference to the drawings and the specific embodiments so that those skilled in the art can better understand the present invention and can implement the present invention, but the embodiments are not to be construed as limiting the present invention, and the embodiments and the technical features of the embodiments can be combined with each other without conflict.
The embodiment of the invention provides a case division method and device based on capability matching, which are used for solving the technical problem of how to divide cases based on capability matching.
Example 1:
as shown in fig. 1, the method for partitioning cases based on capability matching of the present invention comprises the following steps:
s100, constructing a case division rule base, wherein the case division rule base is used for setting data related to personnel capacity evaluation, case classification and case distribution;
s200, constructing a round case rule base, wherein the round case rule base is used for setting and checking the relationship attributes between the related entities of the divided cases, and distributing cases and personnel based on the relationship attributes;
s300, evaluating the ability of the personnel from multiple dimensions based on a case division rule base to obtain an ability evaluation result of each personnel;
s400, constructing heterogeneous association models between cases and personnel, wherein the heterogeneous association models are used for calculating association weights among cases, between personnel and between cases and personnel, and realizing distribution among cases and personnel based on the association weights;
s500, for newly input cases, calculating case entity feature vectors based on case similarity division results;
s600, carrying out data fusion on case entity feature vectors to convert the case entity feature vectors into a new data space, forming a depth fusion entity corresponding to a case, predicting the workload parameters of the case by adopting multilayer linear regression based on the depth fusion, and judging the difficulty and the complexity of the case based on the workload parameters;
s700, distributing the case to the matched personnel based on the case division rule base, the round case rule base, the capability evaluation result of each personnel, the heterogeneous association model and the workload parameters of the case.
The case division reference library in the embodiment is based on historical massive case data, the past experience of case division rules is learned from the case division reference library, the case division process can be quantitatively and objectively measured, the scientificity of case division is reflected, the case division reference library has certain flexibility, and the case division reference library is continuously updated along with the increase of data volume. The case division rule base is provided with data standards, and the data standards comprise performance assessment metadata, case association metadata, case feedback metadata, personnel metadata, case classification metadata and historical case metadata; based on the data standard, a business model is formed, wherein the business model comprises an access integration and visualization business model, a personnel business evaluation business model, a case grouping business model and a case undertaking business model, the access integration and visualization business model is used for setting and displaying related data, the personnel business evaluation business model is used for setting data related to personnel capacity evaluation, the case grouping business model is used for setting data related to case classification, and the case undertaking business model is used for setting data related to case distribution.
The case division criterion library is interconnected with external platforms such as a unified service platform, an inspection personnel system, a case management information system, a performance assessment system and the like, service related data are obtained, and a data standard is obtained.
Based on the practical requirements of specialized case handling and special case division, the round case rule is defined, the accuracy and the specificity of the case division are guaranteed, and some scenes that the two cases can be handled or manual interference is needed are avoided. The related entities for examining and dividing cases comprise case entities, personnel entities and organization entity entities; the case entities comprise regions, categories and the like, the personnel entities comprise names, sexes, ages and the like, the organizations comprise names, service types and the like, the relationship attributes between related entities for checking and dividing cases comprise case relationships, organization relationships, service relationships and the like, the case relationships comprise case types handled by personnel with great proficiency, case types handled by personnel and the like, the organization relationships comprise organizations to which the personnel belong, personnel positions and the like, the service relationships comprise services to which the personnel belong, and the services comprise monitoring, complaints, civil lines, control applications, execution and the like. Meanwhile, the rotation case rule base is used for configuring rule definitions, and the rule definitions comprise: data normalization representation and business rule extraction; rule execution, rule matching, rule priority, and conflict resolution; the rules include defining rules according to roles and defining rules according to organizational personnel.
As shown in fig. 2, in the present embodiment, the ability of a person is evaluated from five dimensions of business ability, case handling quality, active case handling and detection and evaluation, where the business ability includes a academic calendar, a academic position, a job title, etc., the case handling ability includes processed case data, case type number, etc., the case handling quality includes judgment result, compliance with the beginning of a complaint, and judgment, the active case handling includes difficult case tracing, correction, and anti-complaint, and the detection and evaluation includes violation condition, evaluation, and feedback, etc.
And constructing a heterogeneous association model between cases and personnel, wherein the object types comprise personnel, cases, types, processes, regions and the like, and the relationship types comprise the handling between case handling persons and cases, the grouping between cases and the like. The nodes and links in the information network can be added with attributes, entities such as cases and case handling personnel are utilized to establish a correlation network with the attributes, different semantic paths are mined, and potential semantic relationships are extracted. In the aspect of network model calculation, unsupervised clustering analysis is carried out on the basis of feature data of deep fusion in task two, network association weight coefficients are dynamically calculated, and potential association factors among case handling persons, among cases and among cases are discovered. The case characteristic data and the inspection functional characteristic data are fused, model feedback and regulation rules are considered, preference and capability dimensionality of case handling personnel is accurately found, and meanwhile, the characteristics of analysis type cases are mined from multiple dimensionalities such as time, space, cause and effect and activity by using technologies such as correlation analysis.
And after a newly input case is received, calculating a case entity feature vector based on case similarity division results. Specifically, as shown in fig. 3, based on the historical case data in the unified business application system, case information clustering spaces based on regions, fields, numbers of people, case types, case difficulties, coefficients and the like are constructed by adopting natural language processing, case similarity coefficients of different dimensions are obtained by calculation, a case entity system model based on a time sequence mode is extracted, and feature vectors of each case group are calculated to realize case grouping.
Then, calculating workload parameters based on case entity feature vectors, specifically: the case workload evaluation method based on Deep case representation fuses different case data and converts the fused case data into a new data space to form a Deep Fusion Entity (DFE) corresponding to each case, thereby realizing the consistency of the representation of the different types of case data. Based on the DFE entity, the workload parameter of each case is predicted by adopting multilayer linear regression to serve as a basis for judging the difficulty and complexity of the case. The specific working principle of the case workload evaluation method based on depth case characterization is shown in fig. 4.
And finally, distributing the case to the matched personnel based on the case division rule base, the round case rule base, the capability evaluation result of each personnel, the heterogeneous association model and the workload parameters of the case.
Example 2:
the apparatus of the present invention comprises: at least one memory and at least one processor; the at least one memory for storing a machine-readable program; the at least one processor is configured to invoke the machine-readable program to perform the method of embodiment 1.
Example 3:
a computer-readable medium of the present invention, having computer instructions stored thereon, which, when executed by a processor, cause the processor to perform the method disclosed in embodiment 1. Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
It should be noted that not all steps and modules in the above flows and system structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by a plurality of physical entities, or some components in a plurality of independent devices may be implemented together.
While the invention has been shown and described in detail in the drawings and in the preferred embodiments, it is not intended to limit the invention to the embodiments disclosed, and it will be apparent to those skilled in the art that various combinations of the code auditing means in the various embodiments described above may be used to obtain further embodiments of the invention, which are also within the scope of the invention.
Claims (8)
1. A case division method based on personnel ability matching is characterized by comprising the following steps:
constructing a case division rule base, wherein the case division rule base is used for setting data related to personnel capability evaluation, case classification and case distribution;
constructing a round case rule base, wherein the round case rule base is used for setting the relationship attributes among related entities of the inspection branch case, and distributing cases and personnel based on the relationship attributes;
based on a case division criterion library, performing capability evaluation on the personnel from multiple dimensional evaluations to obtain a capability evaluation result of each personnel;
constructing a heterogeneous association model between cases and persons, wherein the heterogeneous association model is used for calculating association weights among the cases, among the persons and between the cases and the persons, and realizing distribution among the cases and the persons based on the association weights;
for newly input cases, calculating case entity feature vectors based on case similarity division results;
the case workload evaluation method based on the deep case representation comprises the steps of carrying out data fusion on case entity feature vectors, converting the case entity feature vectors into a new data space, forming a deep fusion entity corresponding to a case, predicting the workload parameters of the case by adopting multilayer linear regression based on the deep fusion, and judging the difficulty and the complexity of the case based on the workload parameters;
and allocating the case to the matched personnel based on the case division rule base, the round case rule base, the capability evaluation result of each personnel, the heterogeneous association model and the workload parameters of the case.
2. The case division method based on capability matching as claimed in claim 1, wherein the case division criteria library is configured with data criteria, the data criteria comprises performance assessment metadata, case association metadata, case feedback metadata, personnel metadata, case classification metadata and historical case metadata;
based on the data standard, a business model is formed, wherein the business model comprises an access integration and visualization business model, a personnel business evaluation business model, a case grouping business model and a case undertaking business model, the access integration and visualization business model is used for setting and displaying related data, the personnel business evaluation business model is used for setting data related to personnel capacity evaluation, the case grouping business model is used for setting data related to case classification, and the case undertaking business model is used for setting data related to case distribution.
3. The capacity matching-based itemizing method according to claim 1, wherein the examination itemizing related entities include case entities, personnel entities and organization entities;
the case entity comprises regions and categories;
the human entities include name, gender, and age;
the organization comprises a name and a service type;
the relationship attributes among the related entities of the inspection and case sharing comprise case relationships, organization relationships and business relationships;
the case relation comprises a case type which is good for personnel to handle and a case type which is handled by personnel;
the organization relation comprises an organization mechanism to which the personnel belong and personnel positions;
the business relation comprises the business of the personnel.
4. A case division method based on case and ability matching of personnel for checking cases according to claim 1, 2 or 3, characterized in that a round-robin rule base is used for configuring rule definitions, which include:
data normalization representation and business rule extraction;
rule execution, rule matching, rule priority, and conflict resolution;
the rules include defining rules by role and defining rules by organizational personnel.
5. The case dividing method based on capability matching according to claim 1, characterized in that capability evaluation is performed on personnel from five dimensions of business capability, case handling quality, active case handling and inspection and staging evaluation;
the business capability comprises a scholarly calendar, a scholarly position and a title;
the case handling capacity comprises case handling data and case type number;
the case handling quality comprises the consistency of the judgment result and the initiation of the complaint and the judgment;
the active case handling comprises difficult case tracing, correcting and anti-complaint;
the detection evaluation comprises a default condition, evaluation and feedback.
6. The case division method based on capability matching according to claim 1, wherein case entity feature vectors are calculated based on case similarity division results, comprising the steps of:
dividing cases according to regions, fields, numbers of people, types of cases, difficulty and easiness of cases and coefficients, and calculating similarity coefficients in case groups;
dividing the cases according to case grouping conditions and case handling personnel, and calculating a similarity coefficient in a case handling group;
and extracting a case entity information model based on a time sequence mode, and calculating the characteristic vector of each type of case based on the similarity coefficient.
7. An apparatus, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program to perform the method of any of claims 1 to 7.
8. A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1 to 7.
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