CN115730914A - Performance assessment scheme recommendation method and device, storage medium and computer equipment - Google Patents
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Abstract
The application provides a performance assessment scheme recommendation method, a performance assessment scheme recommendation device, a storage medium and computer equipment. The method comprises the following steps: responding to a consultation scene selected by a user, and acquiring a target questionnaire corresponding to the consultation scene from a questionnaire library to collect the required characteristic information of the user; the demand characteristic information comprises current data of a collected target bank, appeal description and problem description; determining a case sorting strategy selected by a user; based on a case sorting strategy, sorting calculation is carried out on the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base, and the cases are sorted according to the calculation result; and determining the cases with the sequence meeting the preset sequence range as recommended cases based on case sequencing results. The method and the system can quickly recommend the design achievement and the thinking of the performance assessment scheme similar to the bank for the consultant, and improve the efficiency of formulating the performance assessment scheme aiming at the target bank.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a performance assessment scheme recommendation method, a performance assessment scheme recommendation device, a storage medium and computer equipment.
Background
The bank performance assessment scheme is formulated by requiring consultants to carry out material carding according to the original materials, questionnaire data, interview results, judgment of project groups and the like of a bank party, so that the assessment scheme is designed after the current situations, problems and appeal of the business and performance assessment aspects of the bank are clarified, and assessment system method documents are compiled.
Disclosure of Invention
The embodiment of the application provides a performance assessment scheme recommendation method, a performance assessment scheme recommendation device, a storage medium and computer equipment, which can provide similar cases to assist consultants in making schemes, improve the working efficiency of scheme design of the consultants and reduce the difficulty of scheme design.
In a first aspect, the present application provides a performance assessment plan recommendation method, the method comprising:
responding to a consultation scene selected by a user, and acquiring a target questionnaire corresponding to the consultation scene from a preset questionnaire library to collect required characteristic information of the user; the demand characteristic information comprises current data of a collected target bank, appeal description and problem description;
determining a case sorting strategy selected by a user;
based on the case sorting strategy, sorting calculation is carried out on the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base, and the cases are sorted according to the calculation result; the case characteristic information comprises case bank current data, an appeal label and a problem label;
and determining the cases with the sequence meeting the preset sequence range as recommended cases based on case sequencing results.
In one embodiment, the case sorting policy is an appeal priority policy, a problem priority policy, an appeal and problem equal importance policy, and a custom tag sorting policy.
In one embodiment, when the case sorting policy is an appeal priority policy, executing the case sorting policy, performing sorting calculation on the demand characteristic information and the case characteristic information of each case in a preset performance assessment scheme case library, and sorting each case according to a calculation result, where the method includes:
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the appeal quantity which is the same as the solved appeal labels of each case bank in the appeal description as the solved appeal matching quantity;
and sequencing the cases from large to small according to the solved appeal matching quantity, the appeal similarity bin value, the problem similarity bin value and the current situation similarity.
In one embodiment, when the case sorting strategy is a problem priority strategy, the method for sorting and calculating the requirement characteristic information and the case characteristic information of each case in the preset performance assessment scheme case base based on the case sorting strategy is executed, and the method for sorting and calculating each case according to the calculation result comprises the following steps:
carrying out logarithmic transformation processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic transformation result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the quantity of the problems in the problem description, which are the same as the solved problem labels of the case banks, as the matching quantity of the solved problems;
and sequencing the cases according to the solved problem matching quantity, the problem similarity binning value, the appeal similarity binning value and the current situation similarity from large to small.
In one embodiment, when the case sorting strategy is an important strategy with the same appeal and the same problem, the case sorting strategy is executed, the case characteristic information of each case in the demand characteristic information and the preset performance assessment scheme case base is sorted and calculated, and the cases are sorted according to the calculation result, including:
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
merging the problem description and the appeal description into a demand description, respectively calculating demand similarity of the demand description and problem labels and appeal labels of each case bank, and carrying out equidistant binning processing on the demand similarity to obtain demand similarity binning values of each case bank and a target bank;
respectively calculating the number of the appeal described by the requirement and the number of the solved appeal labels of each case bank, and taking the sum of the number of the problems described by the requirement and the number of the problems of each case bank which are the same as the number of the solved problems labels as the number of the matched solved requirements of each case bank;
and sequencing the cases from large to small according to the solved requirement matching quantity, the requirement similarity binning value and the current situation similarity.
In one embodiment, when the case sorting policy is a custom sorting policy, executing the case sorting policy, performing sorting calculation on the requirement characteristic information and the case characteristic information of each case in the preset performance assessment scheme case library, and sorting each case according to the calculation result, including:
acquiring a custom tag sequence input by a user;
matching the appeal labels and the problem labels of the cases according to the labels of each sequence in the user-defined label sequence to obtain a binarization matching result of each case;
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
and sequencing the cases in turn according to the binarization matching result and the current situation similarity from big to small.
In one embodiment, the method further comprises:
acquiring case data of each recommended case from the performance assessment scheme case library according to the determined recommended case;
a case recommendation report is generated based on the acquired case data.
In a second aspect, the present application provides a performance assessment plan recommendation device, including:
the system comprises an information acquisition module, a query processing module and a query processing module, wherein the information acquisition module is used for responding to a consultation scene selected by a user, acquiring a target questionnaire corresponding to the consultation scene from a preset questionnaire library and collecting required characteristic information of the user; the demand characteristic information comprises collected target bank current data, appeal description and problem description;
the strategy determining module is used for determining the case sorting strategy selected by the user;
the sorting module is used for sorting and calculating the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base based on the case sorting strategy and sorting each case according to the calculation result; the case characteristic information comprises case bank current data, an appeal label and a problem label;
and the case screening module is used for determining the cases with the sequence meeting the preset sequence range as recommended cases based on case sequencing results.
In a third aspect, the present application provides a storage medium having computer-readable instructions stored therein, which, when executed by one or more processors, cause the one or more processors to perform the steps of the performance assessment plan recommendation method according to any one of the embodiments described above.
In a fourth aspect, the present application provides a computer device comprising: one or more processors, and a memory;
the memory has stored therein computer readable instructions that, when executed by the one or more processors, perform the steps of the performance assessment plan recommendation method of any of the embodiments described above.
According to the technical scheme, the embodiment of the application has the following advantages:
according to the performance assessment scheme recommendation method, the device, the storage medium and the computer equipment, the questionnaire library formed by questionnaires matched with different consultation scenes is preset, when a user selects the consultation scene, the questionnaire corresponding to the consultation scene selected by the user is obtained from the questionnaire library and is used as a target questionnaire to be filled in by the user, so that the required characteristic information of the user on the performance assessment scheme is collected, and the required characteristic information comprises target bank current state data, description and problem description of the performance assessment scheme needing to be formulated; according to a case sorting strategy selected by a user, recommended cases are retrieved from a preset performance assessment scheme case library, sorting calculation is carried out on demand characteristic information and case characteristic information of each case, the cases are sorted according to a calculation result, and the cases which are sorted to meet a preset sequence range are determined as the recommended cases based on the case sorting result.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a schematic flow diagram of a performance assessment plan recommendation method, under an embodiment;
FIG. 2 is a block diagram of a performance assessment plan recommender in one embodiment;
FIG. 3 is a diagram of the internal structure of a computer device in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
As shown in fig. 1, an embodiment of the present application provides a performance assessment plan recommendation method, which includes steps S101 to S104, where:
step S101, responding to a consultation scene selected by a user, and acquiring a target questionnaire corresponding to the consultation scene from a preset questionnaire library to collect required characteristic information of the user.
The demand characteristic information comprises collected target bank current data, appeal description and problem description.
The bank status data includes status quo of business and/or status quo of management, specifically, the status quo of business includes asset scale, deposit scale, loan scale, retail loan proportion, retail loan reject ratio, per capita profit, and the like, and the status quo of management includes regional differences, management levels, business institution characteristics, employee average ages, and the like. The appeal is the planning and the demand of the performance assessment scheme on a macroscopic level, and the problem is the specific problem to be solved aiming at the performance assessment of the current bank.
And step S102, determining a case sorting strategy selected by a user.
In one embodiment, the user-selectable case sorting policy includes an appeal priority policy, a problem priority policy, an appeal and problem equal importance policy, and a custom tag sorting policy, and the user can select the case sorting policy through a consultation page. The appeal priority strategy is to perform relevance ranking from the appeal dimension in a ranking mode in a priority mode. The problem priority strategy is to perform relevance ranking on the dimension needing priority from the problem in the ranking mode. The important strategy that the appeal is equal to the problem is that the priority degrees of the appeal and the problem are not distinguished in the sorting mode, and sorting calculation is carried out according to the equal priority degrees so as to carry out relevance sorting. The user-defined tag sorting strategy is that the user pays attention to specific appeal or problems according to the user, the priority degree of information is considered in the user-defined sorting, sorting calculation is carried out on the basis of the tag sorting defined by the user, and the relevance sorting of cases is realized.
And S103, based on the case sorting strategy, sorting calculation is carried out on the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base, and the cases are sorted according to the calculation result.
The case characteristic information comprises case bank current data, an appeal label and a problem label. And performing structured storage on each case based on case characteristic information in a preset case library, and sequencing each case according to the relevance of the performance assessment requirement of a target bank by adopting a corresponding sequencing calculation mode according to a selected case sequencing strategy. In one embodiment, the case characteristics information also includes a resolved complaint label of the case to a resolved complaint in complaints for the case bank and a resolved problem label of the resolved problem in the problem for the case bank.
And step S104, determining the cases with the sequence meeting the preset order range as recommended cases based on case sequencing results.
The order range of the recommended cases can be configured as required, for example, one case or a plurality of cases with the highest priority are sorted, and then a corresponding number of cases can be recommended according to different requirements, so as to provide reference for consultants.
In the embodiment, a questionnaire library formed by questionnaires matched with different consultation scenes is preset, when a user selects a consultation scene, the questionnaire corresponding to the consultation scene selected by the user is acquired from the questionnaire library and is used as a target questionnaire for the user to fill in so as to collect the required characteristic information of the user on a performance assessment scheme, wherein the required characteristic information comprises target bank current situation data, appeal description and problem description which need to formulate the performance assessment scheme; according to a case sorting strategy selected by a user, recommended cases are retrieved from a preset performance assessment scheme case library, sorting calculation is carried out on demand characteristic information and case characteristic information of each case, the cases are sorted according to a calculation result, and the cases which are sorted to meet a preset sequence range are determined as the recommended cases based on the case sorting result.
In one embodiment, when the case sorting policy is an appeal priority policy, executing the case sorting policy, performing sorting calculation on the demand characteristic information and the case characteristic information of each case in a preset performance assessment scheme case library, and sorting each case according to a calculation result, where the method includes:
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the appeal quantity which is the same as the solved appeal tags of each case bank in the appeal description as the solved appeal matching quantity;
and sequencing the cases from large to small according to the solved appeal matching quantity, the appeal similarity bin value, the problem similarity bin value and the current situation similarity.
Because the current data of different banks are generally large in difference, if the original data are directly adopted for calculation, accuracy which is not beneficial to data analysis can be achieved, the characteristics of the data can be more clearly embodied through logarithmic conversion and normalization processing, the reliability of calculation of the current similarity of a target bank and a case bank is improved, in addition, equidistant binning processing is respectively carried out on appeal similarity and problem similarity, the problem can be effectively avoided by avoiding the problem that the accuracy of recommended cases is influenced due to slight similarity difference in sorting, the data related to calculation of the similarities are huge, and sorting is carried out according to binning values.
In one embodiment, the appeal similarity is a ratio of the same appeal number in the appeal description and the appeal label of the case to a sum of the appeal label of the case, exemplarily, the appeal description includes appeal a, appeal B and appeal C, the appeal label of one case has appeal a, appeal C and appeal D, the same appeal in the appeal description and the appeal label of the case is appeal a and appeal C, that is, the number is 2, and the sum of the appeal in the appeal description and the appeal label of the case is four appeal a, appeal B, appeal C and appeal D, so the appeal similarity is 2/4=50%.
The problem similarity is a ratio of the number of the same problems in the problem description and the problem labels of the cases to the sum of the problems in the problem description and the problem labels of the cases, illustratively, the problem description has problems E, F, and G, wherein the problem label of one case has problems E, H, and J, and then the same problem in the problem description and the problem labels of the cases is problem E, that is, the number is 1, and the sum of the problems in the problem description and the problem labels of the cases is problems E, F, G, H, and J, so the problem similarity is 1/5=25%.
In this embodiment, the solved appeal matching quantity refers to an appeal quantity coinciding with the appeal proposed by the target bank in a case solved appeal tag, and the appeal tag of a case is not necessarily solved in the case, so that the solved appeal needs to be marked during case collection, and subsequent case recommendation calculation is facilitated. For example, if there are demands a, B, and C, the solved demands are a and C, and the target bank proposes demands a and B, the number of the solved demand matches is 1, which is called demand a. The cases are sequentially sorted according to the solved appeal matching number, the appeal similarity bin value, the problem similarity bin value and the current situation similarity from large to small, namely, the cases with the solved appeal matching number are sorted firstly from large to small, if the cases with the solved appeal matching number are the same, the cases with the solved appeal matching number are sorted according to the appeal similarity bin value from large to small, if the cases with the solved appeal matching number are the same in sequence, the problem similarity bin values are introduced for sorting, and similarly, if the cases with the sorted similarity bin value are the same in sequence, the current situation similarity is introduced for sorting, so that case sorting can be realized under the premise that the appeal is considered most firstly.
In one embodiment, when the case sorting strategy is a problem priority strategy, the method for sorting and calculating the requirement characteristic information and the case characteristic information of each case in the preset performance assessment scheme case base based on the case sorting strategy is executed, and the method for sorting and calculating each case according to the calculation result comprises the following steps:
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain appeal similarity binning values of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the quantity of the problems in the problem description, which are the same as the solved problem labels of the case banks, as the matching quantity of the solved problems;
and sequencing the cases from high to low according to the solved problem matching quantity, the problem similarity bin value, the appeal similarity bin value and the current situation similarity bin value.
The solved problem matching quantity in the embodiment refers to the quantity of problems which are overlapped with the problems proposed by the target bank in the case solved problem labels, and the problem labels of the cases are not necessarily solved in the cases, so that the solved problems are marked when the cases are required to be collected, and the case recommendation calculation is convenient to perform subsequently. For example, if a certain case has problems D, E, and F, and the solved problems have problems D and E, and the target bank presents problems D and F, the number of problem matches that the case has solved is 1, which is the problem D. The cases are sequentially sorted according to the problem matching number, the problem similarity bin value, the appeal similarity bin value and the current situation similarity from large to small, namely, the cases with the same problem matching number are sorted according to the problem matching number from large to small firstly, if the cases with the same problem matching number exist, the cases with the same problem matching number are sorted according to the problem similarity bin value from large to small, if the cases with the same problem similarity bin value sorting exist, the appeal similarity bin value is introduced for sorting, and similarly, if the cases with the same sequence still exist, the current situation similarity is introduced for sorting, so that the case sorting can be realized on the premise that the problems are considered firstly.
In one embodiment, when the case sorting strategy is an important strategy with the same appeal and the same problem, the case sorting strategy is executed, the case characteristic information of each case in the demand characteristic information and the preset performance assessment scheme case base is sorted and calculated, and the cases are sorted according to the calculation result, including:
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
merging the problem description and the appeal description into a demand description, respectively calculating demand similarity of the demand description and problem labels and appeal labels of each case bank, and carrying out equidistant binning processing on the demand similarity to obtain a demand similarity binning value of each case bank and a target bank;
respectively calculating the number of the appeal of the demand description which is the same as the solved appeal label of each case bank, and taking the sum of the number of the problems described by the demand description which is the same as the number of the problems of each case bank which are solved as the solved demand matching number of each case bank;
and sequencing the cases from large to small according to the solved requirement matching quantity, the requirement similarity binning value and the current situation similarity.
In the embodiment, the appeal similarity and the problem similarity are not distinguished, the problem description and the appeal description of the target bank are combined into the requirement description, the problem label and the appeal label are also combined for each case, the problem and the appeal are mixed to carry out requirement similarity calculation, namely, during similarity calculation, similarity calculation is not carried out on the problem description and the problem label independently, similarity calculation is not carried out on the appeal description and the appeal label, similarity calculation is carried out on the problem and appeal mixture and the label of the case by taking the requirement description as an object, the priority degrees of the appeal and the problem are not distinguished, and the relevance of the case and the target bank is calculated according to the same priority level. The solved demand matching quantity is the sum of the quantity of the problems in the case-solved problem label which are coincident with the problems in the demand description and the quantity of the demands in the solved demand label which are coincident with the demands in the demand description. For example, if a certain case includes the appeal a, B, and C, the solved appeal a and C, and the target bank proposes the appeal a and B, the number of the solved appeal tags coincident with the appeal in the demand description is 1, that is, the solved appeal a; the case has problems D, E, and F, the solved problems have D and E, the target bank presents problems D and F, and the number of problems in the solved problem label that coincide with the problem in the demand description is 1, which is problem D, and thus the number of solved demand matches is 2.
In one embodiment, the demand similarity is a ratio of the total number of the demands and questions in the demand description to the sum of the demands tags and the question tags of the cases plus the number of the questions, and a ratio of the total number of the demands and the questions in the demand description to the sum of the demands tags and the question tags of the cases, for example, the demands description includes demands a, B, and C and questions D, E, and F, where the demands tags of one case have a, C, and G, the demands in the demand description and the demands tags of the cases have the same demands a and C, that is, the same demands number is 2, and the question tags of the case have E, H, and J, the problems in the problem description and the question tags of the cases have the same questions E, that is the same questions number is 1, so the demands in the demands description and the questions in the demands tags and the questions in the demands tags of the cases and the question tags have the same demands number plus the question number of the questions and the question tags, that is 33.g., the total number of the demands and the demands of the cases plus the questions is 33.g.
The method comprises the steps of sequencing cases according to the solved requirement matching number, the requirement similarity binning value and the current situation similarity from large to small, namely sequencing the cases according to the solved requirement matching number from large to small at first, sequencing the cases with the same solved requirement matching number according to the requirement similarity binning value from large to small if the cases with the same solved requirement matching number exist, and sequencing by introducing the current situation similarity if the cases sequenced on the basis of the requirement similarity binning value have the same sequence, so that case sequencing can be realized on the basis of the strategy with the same importance as the problem in appeal.
In one embodiment, when the case sorting policy is a custom sorting policy, executing the case sorting policy, performing sorting calculation on the requirement characteristic information and the case characteristic information of each case in the preset performance assessment scheme case library, and sorting each case according to the calculation result, including:
acquiring a custom tag sequence input by a user;
matching the appeal labels and the problem labels of the cases according to the labels of each sequence in the user-defined label sequence to obtain a binarization matching result of each case;
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
and sequencing the cases from large to small according to the binarization matching result and the current situation similarity.
In this embodiment, the custom tag sequence refers to a tag priority order determined by a user by ordering priority levels of various complaints and various problems according to requirements, each case is binarized according to the custom tag sequence, matching is performed in sequence according to orders, if an appeal tag or a problem tag of a currently matched case has a tag in any order in the custom tag sequence, the order is binarized to obtain a value 1, and if neither the appeal tag nor the problem tag of the case is a tag matched in the custom tag sequence, the order is binarized to obtain a value 0, and binarization results are arranged according to the custom tag sequence to obtain binarization matching results corresponding to the case. Illustratively, the sequence of the custom tags comprises appeal G, question H, question J and appeal K, the cases are matched, and if the problem tags of the cases comprise appeal G and question J, the binarization matching result of the cases is 1010. When the cases are sorted, sorting is preferentially carried out according to the binarization matching results of the cases, and if the cases with the same rank exist, sorting is carried out by introducing the current situation similarity, so that the case sorting can be realized according to the concerned program of the user to the problem or the appeal.
In one embodiment, the method further comprises:
acquiring case data of each recommended case from the performance assessment scheme case library according to the determined recommended case;
a case recommendation report is generated based on the acquired case data.
The case recommendation method and the case recommendation system can summarize the case data of the recommended cases to generate the case recommendation report, and are convenient for users or consultants to look up.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
The performance assessment plan recommendation device provided by the embodiment of the application is described below, and the performance assessment plan recommendation device described below and the performance assessment plan recommendation method described above can be referred to correspondingly.
As shown in fig. 2, an embodiment of the present application provides a performance assessment plan recommendation device 200, including:
the information acquisition module 201 is configured to respond to a consultation scene selected by a user, and acquire a target questionnaire corresponding to the consultation scene from a preset questionnaire library to collect required characteristic information of the user; the demand characteristic information comprises current data of a collected target bank, appeal description and problem description;
a policy determination module 202, configured to determine a case sorting policy selected by a user;
the sorting module 203 is used for sorting and calculating the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base based on the case sorting strategy, and sorting each case according to the calculation result; the case characteristic information comprises case bank current data, an appeal label and a problem label;
the case screening module 204 is configured to determine, as a recommended case, a case whose ranking satisfies a preset ranking range based on a case ranking result.
In one embodiment, the sorting module is configured to perform the steps of:
when the case sorting strategy is an appeal priority strategy, carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain appeal similarity binning values of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the appeal quantity which is the same as the solved appeal tags of each case bank in the appeal description as the solved appeal matching quantity;
and sequencing the cases from large to small according to the solved appeal matching quantity, the appeal similarity bin value, the problem similarity bin value and the current situation similarity.
In one embodiment, the sorting module is further configured to perform the steps of:
when the case sorting strategy is a problem priority strategy, carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the number of the problems in the problem description, which is the same as the solved problem labels of each case bank, as the matched number of the solved problems;
and sequencing the cases from large to small according to the solved problem matching quantity, the problem similarity bin value, the appeal similarity bin value and the current situation similarity.
In one embodiment, the sorting module is further configured to perform the steps of:
when the case sorting strategy is an important strategy with the same appeal and problem, carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalization data and the case bank normalization data to serve as current situation similarity of the target bank and the case banks;
merging the problem description and the appeal description into a demand description, respectively calculating demand similarity of the demand description and problem labels and appeal labels of each case bank, and carrying out equidistant binning processing on the demand similarity to obtain a demand similarity binning value of each case bank and a target bank;
respectively calculating the number of the appeal of the demand description which is the same as the solved appeal label of each case bank, and taking the sum of the number of the problems of the demand description which is the same as the number of the problems of each case bank which are the same as the solved problem label of each case bank as the matched number of the solved demands of each case bank
And sequencing the cases from large to small according to the solved requirement matching quantity, the requirement similarity binning value and the current situation similarity.
In one embodiment, the sorting module is further configured to perform the steps of:
when the case sorting strategy is a custom sorting strategy, acquiring a custom tag sequence input by a user;
matching the appeal tags and the problem tags of each case according to the tags of each sequence in the user-defined tag sequence to obtain a binarization matching result of each case;
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
and sequencing the cases in turn according to the binarization matching result and the current situation similarity from big to small.
In one embodiment, the performance assessment plan recommendation device further comprises:
the case data acquisition module is used for acquiring case data of each recommended case from the performance assessment scheme case library according to the determined recommended case;
and the report generation module is used for generating a case recommendation report based on the acquired case data.
The division of each module in the performance assessment scheme recommendation device is only used for illustration, and in other embodiments, the performance assessment scheme recommendation device may be divided into different modules as needed to complete all or part of the functions of the performance assessment scheme recommendation device. All or part of the modules in the performance assessment scheme recommending device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, the present application further provides a storage medium having stored therein computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform a performance assessment plan recommendation method as described in any of the above embodiments.
In one embodiment, the present application further provides a computer device having computer-readable instructions stored therein, which when executed by one or more processors, perform the performance assessment plan recommendation method according to any one of the above embodiments.
Illustratively, in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store case data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a performance assessment plan recommendation method.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be 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. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Further, in the description of the present application, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise. Also, as used in this specification, the term "and/or" includes any and all combinations of the associated listed items.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, the embodiments may be combined as needed, and the same and similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A performance assessment plan recommendation method, the method comprising:
responding to a consultation scene selected by a user, and acquiring a target questionnaire corresponding to the consultation scene from a preset questionnaire library to collect required characteristic information of the user; the demand characteristic information comprises current data of a collected target bank, appeal description and problem description;
determining a case sorting strategy selected by a user;
based on the case sorting strategy, sorting calculation is carried out on the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base, and the cases are sorted according to the calculation result; the case characteristic information comprises case bank current data, an appeal label and a problem label;
and determining the cases with the sequence meeting the preset order range as recommended cases based on case sequencing results.
2. The performance assessment plan recommendation method of claim 1, wherein said case ranking policies are an appeal priority policy, a problem priority policy, an appeal and problem equal importance policy, and a custom tag ranking policy.
3. The performance assessment plan recommendation method of claim 2, wherein when the case ranking strategy is an appeal precedence strategy, the case ranking strategy is executed, the case characteristic information of each case in the requirement characteristic information and a preset performance assessment plan case library is ranked and calculated based on the case ranking strategy, and the cases are ranked according to the calculation result, comprising:
carrying out logarithmic transformation processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic transformation result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the appeal quantity which is the same as the solved appeal labels of each case bank in the appeal description as the solved appeal matching quantity;
and sequencing the cases from large to small according to the solved appeal matching quantity, the appeal similarity bin value, the problem similarity bin value and the current situation similarity.
4. The performance assessment plan recommendation method of claim 2, wherein when the case sorting strategy is a problem priority strategy, the case sorting strategy is executed, the case characteristic information of each case in the requirement characteristic information and a preset performance assessment plan case base is sorted and calculated based on the case sorting strategy, and the cases are sorted according to the calculation result, comprising:
carrying out logarithmic transformation processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic transformation result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
respectively calculating the appeal similarity of the appeal description and the appeal label of each case bank, and carrying out equidistant binning processing on the appeal similarity to obtain an appeal similarity binning value of each case bank and a target bank;
respectively calculating the problem similarity of the problem description and the problem label of each case bank, and carrying out equidistant binning processing on the problem similarity to obtain a problem similarity binning value of each case bank and a target bank;
respectively calculating the number of the problems in the problem description, which is the same as the solved problem labels of each case bank, as the matched number of the solved problems;
and sequencing the cases from large to small according to the solved problem matching quantity, the problem similarity bin value, the appeal similarity bin value and the current situation similarity.
5. The performance assessment plan recommendation method of claim 2, wherein when the case sorting strategy is an important strategy with a requirement equal to a problem, the case sorting strategy is executed, the case characteristic information of each case in the case base of the performance assessment plan and the requirement characteristic information are sorted and calculated based on the case sorting strategy, and the cases are sorted according to the calculation result, comprising:
carrying out logarithmic transformation processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic transformation result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
merging the problem description and the appeal description into a demand description, respectively calculating demand similarity of the demand description and problem labels and appeal labels of each case bank, and carrying out equidistant binning processing on the demand similarity to obtain demand similarity binning values of each case bank and a target bank;
respectively calculating the number of the appeal of the demand description which is the same as the solved appeal label of each case bank, and taking the sum of the number of the problems described by the demand description which is the same as the number of the problems of each case bank which are solved as the solved demand matching number of each case bank;
and sequencing the cases from large to small according to the solved requirement matching quantity, the requirement similarity binning value and the current situation similarity.
6. The performance assessment plan recommendation method of claim 2, wherein when the case sorting strategy is a custom sorting strategy, the case sorting strategy is executed, the case characteristic information of each case in the case base of the performance assessment plan and the requirement characteristic information are sorted and calculated, and the cases are sorted according to the calculation result, comprising:
acquiring a custom tag sequence input by a user;
matching the appeal tags and the problem tags of each case according to the tags of each sequence in the user-defined tag sequence to obtain a binarization matching result of each case;
carrying out logarithmic conversion processing on the target bank current data and the case bank current data of each case, and carrying out normalization processing on the target bank current data and the logarithmic conversion result of the case bank current data to obtain target bank normalization data and case bank normalization data;
respectively calculating Euclidean distances between the target bank normalized data and the normalized data of each case bank as the current state similarity between the target bank and each case bank;
and sequencing the cases in turn according to the binarization matching result and the current situation similarity from big to small.
7. The performance assessment plan recommendation method of claim 1, further comprising:
acquiring case data of each recommended case from the performance assessment scheme case library according to the determined recommended case;
a case recommendation report is generated based on the acquired case data.
8. A performance assessment plan recommendation device, comprising:
the system comprises an information acquisition module, a query processing module and a query processing module, wherein the information acquisition module is used for responding to a consultation scene selected by a user, and acquiring a target questionnaire corresponding to the consultation scene from a preset questionnaire library to collect required characteristic information of the user; the demand characteristic information comprises current data of a collected target bank, appeal description and problem description;
the strategy determining module is used for determining a case sorting strategy selected by a user;
the sorting module is used for sorting and calculating the requirement characteristic information and the case characteristic information of each case in a preset performance assessment scheme case base based on the case sorting strategy and sorting each case according to the calculation result; the case characteristic information comprises case bank current data, an appeal label and a problem label;
and the case screening module is used for determining the cases with the sequence meeting the preset sequence range as recommended cases based on case sequencing results.
9. A storage medium, characterized by: the storage medium having stored therein computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the performance assessment plan recommendation method of any one of claims 1 to 7.
10. A computer device, comprising: one or more processors, and a memory;
the memory has stored therein computer-readable instructions that, when executed by the one or more processors, perform the steps of the performance assessment program recommendation method of any of claims 1 to 7.
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