Insurance salesperson ability feature map portraying method and device based on neural network algorithm

Abstract

The invention relates to the technical field of fluid mechanics, and discloses an insurance salesperson ability feature map portraying method based on a neural network algorithm. Comprising the following steps: acquiring salesperson basic data information of salespersons, and preprocessing the salesperson basic data information; the salespersons are clustered through a DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into a plurality of groups; establishing a neural network model, and determining input feature information and output label information; inputting the salesperson basic data information into the neural network model, and generating a salesperson capability feature map portrait according to the salesperson basic data information; the method has the advantages of being high in algorithm precision, simple in program and capable of accurately describing the ability characteristic spectrum portrait.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method and device for profiling the ability characteristics of insurance salesmen based on a neural network algorithm. Background Art

[0002] When insurance companies conduct data analysis, they often analyze salespeople from many different angles, lacking a comprehensive and accurate portrayal of their capabilities. Existing analysis methods are often limited to simple statistical analysis and indicator comparison, making it difficult to deeply explore salespeople's potential capabilities and development directions.

[0003] Therefore, a method and device for profiling the ability characteristics of insurance salesmen based on a neural network algorithm are provided to solve the above-mentioned problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that the existing analysis methods are often limited to simple statistical analysis and indicator comparison, and it is difficult to deeply explore the potential capabilities and development directions of salesmen.

[0005] A first aspect of the present invention provides a method for profiling an insurance salesperson's ability characteristic graph based on a neural network algorithm, and the method for profiling an insurance salesperson's ability characteristic graph based on a neural network algorithm comprises:

[0006] Obtain the basic data information of the salesperson, and pre-process the basic data information of the salesperson;

[0007] The salespersons are clustered using the DBSCAN clustering algorithm, and their basic data information is divided into several groups;

[0008] Establish a neural network model and determine the input feature information and output label information;

[0009] The salesperson’s basic data information is input into the neural network model, and a portrait of the salesperson’s ability characteristics is generated based on the salesperson’s basic data information.

[0010] Furthermore, the obtaining of the basic data information of the salesperson and preprocessing of the basic data information of the salesperson include:

[0011] Obtain the salesperson's basic data information, and extract business sales data, background data, insurance type data, and superior-subordinate evaluation data from the salesperson's basic data information;

[0012] Business sales data, background data, insurance type data and superior-subordinate evaluation data are clarified and standardized respectively.

[0013] Furthermore, the salespersons are clustered by using the DBSCAN clustering algorithm to divide the basic data information of the salespersons into several groups including:

[0014] Perform multi-feature extraction on the pre-processed basic data of the salesperson to generate basic features of the salesperson, features of insurance products, and features of the salesperson's performance;

[0015] The basic characteristics of sales are clustered by the DBSCAN clustering algorithm, and the basic data information of salesmen is divided into several groups, each of which corresponds to a preset label;

[0016] The insurance type characteristics are used to cluster the salespersons through the DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into several groups, each of which corresponds to a preset label;

[0017] The salesperson's performance characteristics are clustered using the DBSCAN clustering algorithm, and the salesperson's basic data information is divided into several groups, each of which corresponds to a preset label.

[0018] Furthermore, the establishing of the neural network model and determining the input feature information and the output label information includes:

[0019] Establishing a neural network model, setting the number of neural network layers of the neural network model to a first preset value, and setting the number of nodes of the neural network model to a second preset value;

[0020] Determining input features of the neural network, wherein the input features include one or more of basic features of the salesperson, features of insurance products, and performance features of the salesperson;

[0021] An output label of the neural network is determined, where the output label is one or more of a preset label extracted from the basic characteristics of the salesperson, a preset label extracted from the characteristics of the insurance type, and a preset label extracted from the performance characteristics of the salesperson.

[0022] Furthermore, the basic data information of the salesperson is input into the neural network model, and the salesperson's ability characteristic map portrait is generated according to the basic data information of the salesperson, including:

[0023] Obtaining basic data information of the salesperson, extracting basic characteristics of the salesperson, characteristics of insurance products and performance characteristics of the salesperson from the basic data information of the salesperson;

[0024] The extracted basic characteristics of the salesperson, characteristics of the insurance type, and performance characteristics of the salesperson are input into the neural network model to generate a number of preset labels;

[0025] A capability characteristic profile of the salesperson is generated based on several preset labels.

[0026] The second aspect of the present invention provides a device for profiling the ability characteristics of insurance salespersons based on a neural network algorithm, comprising:

[0027] A data preprocessing module is used to obtain the basic data information of the salesperson and preprocess the basic data information of the salesperson;

[0028] The clustering module is used to cluster the salespersons using the DBSCAN clustering algorithm and divide the salespersons' basic data information into several groups;

[0029] A neural network model building module is used to build a neural network model and determine input feature information and output label information;

[0030] The graph portrait generation module is used to input the basic data information of the salesperson into the neural network model and generate a graph portrait of the salesperson's ability characteristics based on the salesperson's basic data information.

[0031] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions;

[0032] The at least one processor calls the instructions in the memory so that the electronic device executes the various steps of the insurance salesperson capability feature map profiling method based on the neural network algorithm as described above.

[0033] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the method for profiling the ability characteristic map of insurance salesmen based on a neural network algorithm as described above.

[0034] The present invention can conduct a comprehensive analysis of the ability characteristics of salesmen, covering multiple aspects such as the salesmen's own abilities, customer relationship abilities, product sales abilities, etc., and provide insurance companies with more comprehensive sales personnel portrait information; the present invention adopts a polynomial neural network algorithm for analysis, which can effectively identify the potential abilities and development directions of salesmen and improve the accuracy of the analysis results; the present invention provides a visual display function, which can intuitively display the ability characteristics of each salesman, facilitating insurance companies to conduct analysis and decision-making; the present invention can be applied to multiple aspects of insurance companies such as personnel management, performance improvement, personnel allocation and training, and has broad application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a method for profiling an insurance salesperson's capability profile based on a neural network algorithm provided in the first embodiment of the present invention;

[0036] Figure 2A flowchart of a method for profiling an insurance salesperson's capability profile based on a neural network algorithm provided in a second embodiment of the present invention;

[0037] Figure 3 A flowchart of a method for profiling an insurance salesperson's capability profile based on a neural network algorithm provided in a third embodiment of the present invention;

[0038] Figure 4 A schematic diagram of the structure of a device for profiling an insurance salesperson's ability characteristic graph based on a neural network algorithm provided in an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiment of the present invention provides a method for generating a capability characteristic map portrait of an insurance salesperson based on a neural network algorithm, including obtaining basic data information of the salesperson, preprocessing the basic data information of the salesperson; clustering the salesperson through the DBSCAN clustering algorithm, and dividing the basic data information of the salesperson into several groups; establishing a neural network model, determining input characteristic information and output label information; inputting the basic data information of the salesperson into the neural network model, and generating a capability characteristic map portrait of the salesperson according to the basic data information of the salesperson. The present invention solves the problem in the prior art that the analysis method is often limited to simple statistical analysis and indicator comparison, and it is difficult to deeply explore the potential capabilities and development directions of the salesperson.

[0041] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the insurance salesperson capability feature graph profiling method based on the neural network algorithm in the embodiment of the present invention includes:

[0043] Obtain the basic data information of the salesperson, and pre-process the basic data information of the salesperson; specifically, it includes: obtaining the basic data information of the salesperson, extracting the business sales data, background data, insurance type data and superior-subordinate evaluation data from the basic data information of the salesperson; performing data clarification and standardization processing on the business sales data, background data, insurance type data and superior-subordinate evaluation data respectively; this step is mainly used to collect various data of the salesperson, including the salesperson's own information (education, age, years of work, etc.), customer visit records, insurance order records, etc., and perform data cleaning and standardization processing to ensure the integrity and consistency of the data;

[0044] The salespersons are clustered using the DBSCAN clustering algorithm, and their basic data information is divided into several groups;

[0045] Establish a neural network model and determine the input feature information and output label information;

[0046] The salesperson’s basic data information is input into the neural network model, and a portrait of the salesperson’s ability characteristics is generated based on the salesperson’s basic data information.

[0047] See also Figure 2 The second embodiment of the insurance salesperson capability feature graph profiling method based on the neural network algorithm in the embodiment of the present invention includes:

[0048] Obtain the basic data information of the salesperson, and pre-process the basic data information of the salesperson; specifically, it includes: obtaining the basic data information of the salesperson, extracting the business sales data, background data, insurance type data and superior-subordinate evaluation data from the basic data information of the salesperson; performing data clarification and standardization processing on the business sales data, background data, insurance type data and superior-subordinate evaluation data respectively; this step is mainly used to collect various data of the salesperson, including the salesperson's own information (education, age, years of work, etc.), customer visit records, insurance order records, etc., and perform data cleaning and standardization processing to ensure the integrity and consistency of the data;

[0049] The salespersons are clustered using the DBSCAN clustering algorithm, and their basic data information is divided into several groups;

[0050] Specifically, it includes: extracting multiple features from the pre-processed basic data of the salesperson to generate basic features of the salesperson, features of insurance products and features of the salesperson's performance;

[0051] The basic characteristics of sales are clustered by the DBSCAN clustering algorithm, and the basic data information of salesmen is divided into several groups, each of which corresponds to a preset label;

[0052] The insurance type characteristics are used to cluster the salespersons through the DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into several groups, each of which corresponds to a preset label;

[0053] The salesperson's performance characteristics are clustered using the DBSCAN clustering algorithm, and the salesperson's basic data information is divided into several groups, each of which corresponds to a preset label.

[0054] The DBSCAN algorithm is a density-based clustering algorithm that can effectively identify different clustering structures in the data and filter out noise data.

[0055] Establish a neural network model and determine the input feature information and output label information;

[0056] The salesperson’s basic data information is input into the neural network model, and a portrait of the salesperson’s ability characteristics is generated based on the salesperson’s basic data information.

[0057] See also Figure 3 The third embodiment of the insurance salesperson capability feature graph profiling method based on the neural network algorithm in the embodiment of the present invention includes:

[0058] Obtain the basic data information of the salesperson, and pre-process the basic data information of the salesperson; specifically, it includes: obtaining the basic data information of the salesperson, extracting the business sales data, background data, insurance type data and superior-subordinate evaluation data from the basic data information of the salesperson; performing data clarification and standardization processing on the business sales data, background data, insurance type data and superior-subordinate evaluation data respectively; this step is mainly used to collect various data of the salesperson, including the salesperson's own information (education, age, years of work, etc.), customer visit records, insurance order records, etc., and perform data cleaning and standardization processing to ensure the integrity and consistency of the data;

[0059] The salespersons are clustered using the DBSCAN clustering algorithm, and their basic data information is divided into several groups;

[0060] Specifically, it includes: extracting multiple features from the pre-processed basic data of the salesperson to generate basic features of the salesperson, features of insurance products and features of the salesperson's performance;

[0061] The basic characteristics of sales are clustered by the DBSCAN clustering algorithm, and the basic data information of salesmen is divided into several groups, each of which corresponds to a preset label;

[0062] The insurance type characteristics are used to cluster the salespersons through the DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into several groups, each of which corresponds to a preset label;

[0063] The salesperson's performance characteristics are clustered using the DBSCAN clustering algorithm, and the salesperson's basic data information is divided into several groups, each of which corresponds to a preset label.

[0064] The DBSCAN algorithm is a density-based clustering algorithm that can effectively identify different clustering structures in the data and filter out noise data.

[0065] Establish a neural network model and determine the input feature information and output label information;

[0066] Specifically include:

[0067] Establishing a neural network model, setting the number of neural network layers of the neural network model to a first preset value, and setting the number of nodes of the neural network model to a second preset value;

[0068] Determining input features of the neural network, wherein the input features include one or more of basic features of the salesperson, features of insurance products, and performance features of the salesperson;

[0069] An output label of the neural network is determined, where the output label is one or more of a preset label extracted from the basic characteristics of the salesperson, a preset label extracted from the characteristics of the insurance type, and a preset label extracted from the performance characteristics of the salesperson.

[0070] Input the basic data information of the salesperson into the neural network model, and generate a characteristic graph of the salesperson's ability based on the basic data information of the salesperson;

[0071] Specifically, it includes obtaining basic data information of the salesperson, extracting basic characteristics of the salesperson, characteristics of insurance products and performance characteristics of the salesperson from the basic data information of the salesperson;

[0072] The extracted basic characteristics of the salesperson, characteristics of the insurance type, and performance characteristics of the salesperson are input into the neural network model to generate a number of preset labels;

[0073] A capability characteristic spectrum portrait of the salesperson may be generated based on the generated plurality of preset tags, and the capability characteristic spectrum portrait may be displayed through a display device.

[0074] The above describes the insurance salesperson capability characteristic graph profiling method based on the neural network algorithm in the embodiment of the present invention. The following describes the insurance salesperson capability characteristic graph profiling device based on the neural network algorithm in the embodiment of the present invention. Figure 4 In the embodiment of the present invention, the insurance salesperson capability characteristic graph profiling device based on the neural network algorithm includes:

[0075] The data preprocessing module 401 is used to obtain the basic data information of the salesperson and preprocess the basic data information of the salesperson;

[0076] A clustering module 402 is used to cluster the salespersons using a DBSCAN clustering algorithm, and divide the basic data information of the salespersons into several groups;

[0077] A neural network model building module 403 is used to build a neural network model and determine input feature information and output label information;

[0078] The graph portrait generation module 404 is used to input the basic data information of the salesperson into the neural network model and generate a graph portrait of the salesperson's ability characteristics based on the basic data information of the salesperson.

[0079] above Figure 5 The insurance salesperson capability feature map profiling device based on a neural network algorithm in an embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in an embodiment of the present invention is described in detail from the perspective of hardware processing.

[0080] Figure 5 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0081] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 5 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0082] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of a method for profiling the ability characteristic map of an insurance salesperson using a neural network algorithm.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0085] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for profiling the ability characteristics of insurance salespersons based on a neural network algorithm, characterized in that: The insurance salesperson capability feature map profiling method based on the neural network algorithm includes: Obtain the basic data information of the salesperson, and pre-process the basic data information of the salesperson; The salespersons are clustered using the DBSCAN clustering algorithm, and their basic data information is divided into several groups; Establish a neural network model and determine the input feature information and output label information; The salesperson’s basic data information is input into the neural network model, and a portrait of the salesperson’s ability characteristics is generated based on the salesperson’s basic data information.

2. The method for profiling the ability characteristics of insurance salespersons based on a neural network algorithm according to claim 1 is characterized in that: The obtaining of the basic data information of the salesperson and preprocessing of the basic data information of the salesperson include: Obtain the salesperson's basic data information, and extract business sales data, background data, insurance type data, and superior-subordinate evaluation data from the salesperson's basic data information; Business sales data, background data, insurance type data and superior-subordinate evaluation data are clarified and standardized respectively.

3. The method for profiling the ability characteristics of insurance salespersons based on a neural network algorithm according to claim 2 is characterized in that: The salespersons are clustered by the DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into several groups including: Perform multi-feature extraction on the pre-processed basic data of the salesperson to generate basic features of the salesperson, features of insurance products, and features of the salesperson's performance; The basic characteristics of sales are clustered by the DBSCAN clustering algorithm, and the basic data information of salesmen is divided into several groups, each of which corresponds to a preset label; The insurance type characteristics are used to cluster the salespersons through the DBSCAN clustering algorithm, and the basic data information of the salespersons is divided into several groups, each of which corresponds to a preset label; The salesperson's performance characteristics are clustered using the DBSCAN clustering algorithm, and the salesperson's basic data information is divided into several groups, each of which corresponds to a preset label.

4. The method for profiling the ability characteristics of insurance salespersons based on a neural network algorithm according to claim 3 is characterized in that: The establishing of the neural network model and determining the input feature information and the output label information comprises: Establishing a neural network model, setting the number of neural network layers of the neural network model to a first preset value, and setting the number of nodes of the neural network model to a second preset value; Determining input features of the neural network, wherein the input features include one or more of basic features of the salesperson, features of insurance products, and performance features of the salesperson; An output label of the neural network is determined, where the output label is one or more of a preset label extracted from the basic characteristics of the salesperson, a preset label extracted from the characteristics of the insurance type, and a preset label extracted from the performance characteristics of the salesperson.

5. The method for profiling the ability characteristics of insurance salespersons based on a neural network algorithm according to claim 4 is characterized in that: Input the basic data information of the salesperson into the neural network model, and generate the salesperson's ability characteristic map portrait based on the basic data information of the salesperson, including: Obtaining basic data information of the salesperson, extracting basic characteristics of the salesperson, characteristics of insurance products and performance characteristics of the salesperson from the basic data information of the salesperson; The extracted basic characteristics of the salesperson, characteristics of the insurance type, and performance characteristics of the salesperson are input into the neural network model to generate a number of preset labels; A capability characteristic profile of the salesperson is generated based on several preset labels.

6. A device for profiling the ability characteristics of insurance salespersons based on a neural network algorithm, characterized in that: include: A data preprocessing module is used to obtain the basic data information of the salesperson and preprocess the basic data information of the salesperson; The clustering module is used to cluster the salespersons using the DBSCAN clustering algorithm and divide the salespersons' basic data information into several groups; A neural network model building module is used to build a neural network model and determine input feature information and output label information; The graph portrait generation module is used to input the basic data information of the salesperson into the neural network model and generate a graph portrait of the salesperson's ability characteristics based on the salesperson's basic data information.

7. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the method for profiling the ability characteristics of insurance salesmen based on a neural network algorithm as described in any one of claims 1 to 7.

8. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the method for profiling the ability characteristics of insurance salesmen based on a neural network algorithm as claimed in any one of claims 1 to 7 are implemented.

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