CN114996386A - Business role identification method, device, equipment and storage medium - Google Patents
Business role identification method, device, equipment and storage medium Download PDFInfo
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- CN114996386A CN114996386A CN202210583187.7A CN202210583187A CN114996386A CN 114996386 A CN114996386 A CN 114996386A CN 202210583187 A CN202210583187 A CN 202210583187A CN 114996386 A CN114996386 A CN 114996386A
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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Abstract
The invention relates to an artificial intelligence technology, and discloses a service role identification method, which comprises the following steps: classifying the sources of the service data sets to obtain a plurality of service subsets of different sources; extracting a service subset which accords with a preset source condition from a plurality of service subsets as a target subset, performing initial role identification on the target subset to obtain a role identification result, namely that a target service user is a first role, performing role verification on the target service user, and determining the target service user as the first role when the role verification is passed; the role recognition result is that the target service user is a second role, and keywords of target fields in a plurality of target subsets are extracted; and performing role segmentation on the target service user according to the keywords to obtain the role of the target service user. In addition, the invention also relates to a block chain technology, and the target subset can be stored in a node of the block chain. The invention also provides a service role recognition device, electronic equipment and a storage medium. The invention can improve the efficiency of service role identification.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for identifying a service role, an electronic device, and a computer-readable storage medium.
Background
In different business fields, the order output is a key part in the business, the workload of the order output operators is reduced and the order output efficiency is improved by simplifying the order output process, so that the purposes of improving the productivity of the order output personnel and reducing the operation cost of a company can be achieved, and the accurate identification of the role of the individual personnel is the primary condition for simplifying the order output process.
In the traditional method for identifying the roles of the business personnel, the roles to which the current business personnel belong are obtained by inquiring and comparing a business role database, and the method needs to compare the current business personnel with data in the business role database one by one so as to confirm the business roles, so that the efficiency of identifying the business roles is low.
Disclosure of Invention
The invention provides a method and a device for identifying a service role and a computer readable storage medium, and mainly aims to improve the efficiency of identifying the service role.
In order to achieve the above object, a method for identifying a service role provided by the present invention comprises:
acquiring a service data set, and classifying data sources of the service data set to obtain a plurality of service subsets from different sources;
extracting a plurality of service subsets which accord with preset source conditions from the service subsets to serve as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
when the role identification result indicates that the target service user corresponding to the target subset is a first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role;
when the role identification result is that the target service user corresponding to the target subset is a second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields;
and performing role segmentation on the target service user according to the keywords to obtain a role corresponding to the target service user.
Optionally, the classifying the data sources of the service data set to obtain service subsets from a plurality of different sources includes:
standardizing the service data set to obtain a standard data set;
performing source identification on the standard data set by using a pre-trained source identification model to obtain sources corresponding to a plurality of standard data in the standard data set;
and clustering the data with the same source to obtain a plurality of service subsets with different sources.
Optionally, the normalizing the service data set to obtain a standard data set includes:
performing data cleaning on the service data in the service data set to obtain a cleaning data set;
and reserving the data which accords with the preset configuration rule in the cleaning data set as a standard data set.
Optionally, the performing role segmentation on the target service user according to the keyword to obtain a role corresponding to the target service user includes:
performing keyword label marking on the target service user based on the keyword;
and searching a key role corresponding to the keyword label in a preset keyword role library, and taking the key role as a role corresponding to the target service user.
Optionally, the extracting keywords from the plurality of target fields includes:
collecting a training data set and constructing a deep neural network;
training the deep neural network by using the training data set to obtain a trained keyword extraction model;
and outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields.
Optionally, the performing role verification on the target service user includes:
acquiring corresponding role information in a preset role information reference library based on the first role;
and performing information verification on the target service user according to the role information, and determining the target service user as a first role when the information verification is passed.
Optionally, the performing initial role identification on the target subset to obtain a role identification result includes:
comparing the target subset with data in a preset personal information base, and taking a personal role corresponding to the data consistent with the target subset as an initial role of a target service user corresponding to the target subset;
and performing initial verification on the target subset according to the related information of the initial role, and determining that the initial role is the role corresponding to the target service user corresponding to the target subset when the initial verification is passed.
In order to solve the above problem, the present invention further provides a service role identification apparatus, including:
the source classification module is used for acquiring a service data set, and performing data source classification on the service data set to obtain a plurality of service subsets from different sources;
the initial identification module is used for extracting a plurality of service subsets which accord with a preset source condition from the service subsets to serve as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
the role verification module is used for performing role verification on the target service user when the role recognition result indicates that the target service user corresponding to the target subset is a first role, and determining the target service user as the first role when the role verification is passed;
and the role subdivision module is used for extracting a plurality of target fields in the target subset, extracting keywords from the target fields and carrying out role subdivision on the target service user according to the keywords to obtain the role corresponding to the target service user when the role identification result shows that the target service user corresponding to the target subset is the second role.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the service role identification method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the service role identification method described above.
The embodiment of the invention obtains a plurality of service subsets from different sources by classifying the data sources of the service data set, wherein the data source classification can be used as a basis for preliminarily dividing the service data set, and extracts the service subsets which accord with the preset source conditions from the plurality of service subsets as the target subsets, thereby ensuring the source accuracy of the target subsets. And performing initial role identification on the target subset to obtain role identification results, and performing role verification or role subdivision according to the role identification results respectively, so that the role identification efficiency can be improved under the condition of ensuring the accuracy of role identification. Therefore, the service role identification method, the service role identification device, the electronic equipment and the computer readable storage medium provided by the invention can solve the problem that the efficiency of service role identification is not high enough.
Drawings
Fig. 1 is a schematic flow chart of a service role identification method according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a service role recognition apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the service role identification method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a service role identification method. The execution subject of the service role identification method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the service role identification method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a service role identification method according to an embodiment of the present invention. In this embodiment, the service role identification method includes:
s1, acquiring a service data set, and classifying the data sources of the service data set to obtain a plurality of service subsets of different sources.
In the embodiment of the invention, the business data set comprises the invoice data from different system sources such as an internal company docking platform, an external company docking platform and an internal collective docking platform. Since data of different system sources are operated by different service personnel, the system sources can be used as initial references for role identification.
Specifically, the classifying the data sources of the service data set to obtain service subsets from a plurality of different sources includes:
standardizing the service data set to obtain a standard data set;
performing source identification on the standard data set by using a pre-trained source identification model to obtain sources corresponding to a plurality of standard data in the standard data set;
and clustering the data with the same source to obtain a plurality of service subsets with different sources.
Further, the normalizing the service data set to obtain a standard data set includes:
performing data cleaning on the service data in the service data set to obtain a cleaning data set;
and reserving the data which accords with the preset configuration rule in the cleaning data set as a standard data set.
In detail, the data cleansing includes performing a deletion operation on an abnormal value in the business data set and performing a completion operation on an insufficient value in the business data set. The business data can be subjected to anomaly detection by using a java statement with anomaly detection. When missing values exist in the service data set, the embodiment of the present invention may perform completion operation on the service data set by using an existing missing value filling method, where the existing missing value filling method includes, but is not limited to, filling default values, mean values, modes, and KNN.
In the scheme, the configuration rule is to delete a blank field in the service data.
Specifically, before the source recognition is performed on the standard data set by using the pre-trained source recognition model, the method further includes:
inputting the standard data set into a preset source identification model for source prediction to obtain a predicted source label;
comparing the predicted source label with a preset real source label;
when the predicted source label is consistent with the real source label, outputting the source identification model as a pre-trained source identification model;
when the predicted source label is inconsistent with the real source label, carrying out model parameter adjustment on the source identification model to obtain an adjusted source identification model;
and inputting the standard data set into the adjusted source identification model for source prediction to obtain a new predicted source label, and when the new predicted source label is consistent with the real source label, taking the adjusted source identification model as a trained source identification model.
In detail, the preset source identification model may be a convolutional neural network model.
Specifically, the inputting the standard data set into a preset source identification model for source prediction to obtain a predicted source tag includes:
performing convolution processing and pooling processing on the standard data set by using a convolution layer and a pooling layer in the source identification model to obtain a characteristic data set;
inputting the characteristic data set into a preset activation function to obtain an activation probability corresponding to the characteristic data set;
and obtaining a corresponding prediction source label according to the activation probability and a preset label reference table.
Further, the data with the same source are clustered to obtain a plurality of service subsets with different sources.
S2, extracting the service subsets which accord with the preset source conditions from the plurality of service subsets as target subsets, and performing initial role identification on the target subsets to obtain role identification results.
In the embodiment of the present invention, the preset source condition may be an internal condition of a company, and the service subset meeting the preset source condition in the plurality of service subsets is extracted as a target subset, that is, the service subset whose data source is the internal condition of the company in the plurality of service subsets is extracted as a target subset.
Specifically, the performing initial role identification on the target subset to obtain a role identification result includes:
comparing the target subset with data in a preset personal information base, and taking a personal role corresponding to the data consistent with the target subset as an initial role of a target service user corresponding to the target subset;
and performing initial verification on the target subset according to the related information of the initial role, and determining that the initial role is the role corresponding to the target service user corresponding to the target subset when the initial verification is passed.
In detail, the personal information base includes a plurality of employee information, and the related information of the initial role is basic identity information and the like related to the initial role.
For example, the target subset is compared with data in a preset personal information base, and a business line to which the employee in the employee information belongs judges whether the employee is a salesperson or a commuter; and the role of the order recording personnel, such as whether the order is a business or not, or whether the order is a business person, is preferentially judged by using the staff UM account number, the staff Chinese name, the associated order recording operator and other related information in the staff information table.
S3, when the role recognition result is that the target service user corresponding to the target subset is the first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role.
In the embodiment of the invention, the first role is a service staff. Namely, when the role identification result indicates that the target service user corresponding to the target subset is the first role, the role of the target service user is verified.
Specifically, the role verification of the target service user includes:
acquiring corresponding role information in a preset role information reference library based on the first role;
and performing information verification on the target service user according to the role information, and determining the target service user as a first role when the information verification is passed.
In detail, the role information reference library includes different roles and role information corresponding to the different roles.
And S4, when the role recognition result shows that the target service user corresponding to the target subset is the second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields.
In this embodiment of the present invention, the second role may be a client. Further subdivision is possible because the customers may be divided into different roles, such as agents or brokers.
Specifically, the extracting a plurality of target fields in the target subset includes:
searching the name index in the target subset;
and taking the fields under the name index as a plurality of target fields in the target subset.
In detail, data included under a name index in the target subset is used as a plurality of target fields in the target subset, for example, the name index is "customer name", and the target field is third person of agent.
Further, the extracting keywords from the plurality of target fields includes:
collecting a training data set and constructing a deep neural network;
training the deep neural network by using the training data set to obtain a trained keyword extraction model;
and outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields.
In particular, a training data set may be collected using crawler technology. And outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields, wherein the keywords can be keywords 'agent' and keywords 'economic'.
S5, performing role segmentation on the target service user according to the keywords to obtain a role corresponding to the target service user.
In the embodiment of the present invention, the performing role segmentation on the target service user according to the keyword to obtain a role corresponding to the target service user includes:
performing keyword label marking on the target service user based on the keyword;
and searching a key role corresponding to the keyword label in a preset keyword role library, and taking the key role as a role corresponding to the target service user.
In detail, if the keyword may be "agent" or "economy", the keyword "agent" or "economy" is used as the keyword tag of the target business user, and the keyword role library includes a plurality of role names related to the keyword tag, similar to "agent" or "broker". And searching a key role agent corresponding to the key word label agent in a preset key word role library, and taking the key role agent as a role corresponding to the target service user.
The embodiment of the invention obtains a plurality of service subsets from different sources by classifying the data sources of the service data set, wherein the data source classification can be used as a basis for preliminarily dividing the service data set, and extracts the service subsets which accord with the preset source conditions from the plurality of service subsets as the target subsets, thereby ensuring the source accuracy of the target subsets. And performing initial role identification on the target subset to obtain role identification results, and performing role verification or role subdivision according to the role identification results respectively, so that the role identification efficiency can be improved under the condition of ensuring the accuracy of role identification. Therefore, the service role identification method provided by the invention can solve the problem that the efficiency of service role identification is not high enough.
Fig. 2 is a functional block diagram of a service role recognition apparatus according to an embodiment of the present invention.
The service role recognition apparatus 100 according to the present invention may be installed in an electronic device. According to the implemented functions, the service role recognition device 100 may include a source classification module 101, an initial recognition module 102, a role verification module 103, and a role segmentation module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the source classification module 101 is configured to obtain a service data set, and perform data source classification on the service data set to obtain a plurality of service subsets from different sources;
the initial identification module 102 is configured to extract a service subset, which meets a preset source condition, from the plurality of service subsets as a target subset, and perform initial role identification on the target subset to obtain a role identification result;
the role verification module 103 is configured to perform role verification on the target service user when the role identification result indicates that the target service user corresponding to the target subset is the first role, and determine the target service user as the first role when the role verification passes;
the role subdivision module 104 is configured to, when the role identification result indicates that the target service user corresponding to the target subset is a second role, extract a plurality of target fields in the target subset, perform keyword extraction on the plurality of target fields, perform role subdivision on the target service user according to the keywords, and obtain a role corresponding to the target service user.
In detail, the specific implementation of each module of the service role recognition apparatus 100 is as follows:
step one, acquiring a service data set, and classifying data sources of the service data set to obtain a plurality of service subsets of different sources.
In the embodiment of the invention, the business data set comprises the invoice data from different system sources such as an internal company docking platform, an external company docking platform and an internal collective docking platform. Since data from different system sources are handled by different business personnel, the system sources can be used as initial references for role identification.
Specifically, the classifying the data sources of the service data set to obtain service subsets from a plurality of different sources includes:
standardizing the service data set to obtain a standard data set;
performing source identification on the standard data set by using a pre-trained source identification model to obtain sources corresponding to a plurality of standard data in the standard data set;
and clustering the data with the same source to obtain a plurality of service subsets with different sources.
Further, the normalizing the service data set to obtain a standard data set includes:
performing data cleaning on the service data in the service data set to obtain a cleaning data set;
and reserving the data which accords with the preset configuration rule in the cleaning data set as a standard data set.
In detail, the data cleansing includes performing a deletion operation on an abnormal value in the service data set and performing a completion operation on an insufficient value in the service data set. The abnormal detection can be carried out on the service data by using a java statement with abnormal detection. When missing values exist in the service data set, the embodiment of the present invention may perform completion operation on the service data set by using an existing missing value filling method, where the existing missing value filling method includes, but is not limited to, filling default values, mean values, modes, and KNN.
In the scheme, the configuration rule is to delete a blank field in the service data.
Specifically, before performing source identification on the standard data set by using the pre-trained source identification model, further performing:
inputting the standard data set into a preset source identification model for source prediction to obtain a predicted source label;
comparing the predicted source label with a preset real source label;
when the predicted source label is consistent with the real source label, outputting the source identification model as a pre-trained source identification model;
when the predicted source label is inconsistent with the real source label, carrying out model parameter adjustment on the source identification model to obtain an adjusted source identification model;
and inputting the standard data set into the adjusted source identification model for source prediction to obtain a new predicted source label, and when the new predicted source label is consistent with the real source label, taking the adjusted source identification model as a trained source identification model.
In detail, the preset source identification model may be a convolutional neural network model.
Specifically, the inputting the standard data set into a preset source identification model for source prediction to obtain a predicted source tag includes:
performing convolution processing and pooling processing on the standard data set by using a convolution layer and a pooling layer in the source identification model to obtain a characteristic data set;
inputting the characteristic data set into a preset activation function to obtain an activation probability corresponding to the characteristic data set;
and obtaining a corresponding prediction source label according to the activation probability and a preset label reference table.
Further, the data with the same source are clustered to obtain a plurality of service subsets with different sources.
And step two, extracting the service subsets which accord with the preset source conditions from the plurality of service subsets as target subsets, and performing initial role identification on the target subsets to obtain role identification results.
In the embodiment of the present invention, the preset source condition may be an internal condition of a company, and the service subset meeting the preset source condition in the plurality of service subsets is extracted as a target subset, that is, the service subset whose data source is the internal condition of the company in the plurality of service subsets is extracted as a target subset.
Specifically, the performing initial role identification on the target subset to obtain a role identification result includes:
comparing the target subset with data in a preset personal information base, and taking a personal role corresponding to the data consistent with the target subset as an initial role of a target service user corresponding to the target subset;
and performing initial verification on the target subset according to the related information of the initial role, and determining that the initial role is the role corresponding to the target service user corresponding to the target subset when the initial verification is passed.
In detail, the personal information base includes a plurality of employee information, and the related information of the initial role is basic identity information related to the initial role.
For example, the target subset is compared with data in a preset personal information base, and a business line to which the employee in the employee information belongs judges whether the employee is a salesperson or a commuter; and (4) preferentially judging the role of the order recording personnel, such as whether the order recording personnel is an attendance or a salesman, by respectively using related information, such as an employee UM account number, an employee Chinese name, a related order recording operator and the like in the employee information table.
And step three, when the role identification result indicates that the target service user corresponding to the target subset is the first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role.
In the embodiment of the invention, the first role is a service staff. Namely, when the role identification result indicates that the target service user corresponding to the target subset is the first role, the role verification is carried out on the target service user.
Specifically, the role verification of the target service user includes:
acquiring corresponding role information in a preset role information reference library based on the first role;
and performing information verification on the target service user according to the role information, and determining the target service user as a first role when the information verification is passed.
In detail, the role information reference library includes different roles and role information corresponding to the different roles.
And step four, when the role recognition result is that the target service user corresponding to the target subset is the second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields.
In this embodiment of the present invention, the second role may be a client. The client can be divided into different roles such as agent or broker, so that further subdivision can be performed.
Specifically, the extracting a plurality of target fields in the target subset includes:
searching the name index in the target subset;
and taking the fields under the name index as a plurality of target fields in the target subset.
In detail, data included under a name index in the target subset is used as a plurality of target fields in the target subset, for example, the name index is "customer name", and the target field is third person of agent.
Further, the extracting the keywords from the plurality of target fields includes:
collecting a training data set and constructing a deep neural network;
training the deep neural network by using the training data set to obtain a trained keyword extraction model;
and outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields.
In particular, a training data set may be collected using crawler technology. And outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields, wherein the keywords can be keywords 'agent' and keywords 'economic'.
And fifthly, carrying out role segmentation on the target service user according to the keyword to obtain a role corresponding to the target service user.
In the embodiment of the present invention, the performing role segmentation on the target service user according to the keyword to obtain a role corresponding to the target service user includes:
performing keyword label marking on the target service user based on the keyword;
and searching a key role corresponding to the keyword label in a preset keyword role library, and taking the key role as a role corresponding to the target service user.
In detail, if the keyword may be "agent" or "economic", the keyword "agent" or "economic" is used as the keyword tag of the target business user, and the keyword role library includes a plurality of role names related to the keyword tag, similar to "agent" or "broker". And searching a key role agent corresponding to the key word label agent in a preset key word role library, and taking the key role agent as a role corresponding to the target service user.
The embodiment of the invention obtains a plurality of service subsets from different sources by classifying the data sources of the service data set, wherein the data source classification can be used as a basis for preliminarily dividing the service data set, and extracts the service subsets which accord with the preset source conditions from the plurality of service subsets as the target subsets, thereby ensuring the source accuracy of the target subsets. And performing initial role identification on the target subset to obtain role identification results, and performing role verification or role subdivision according to the role identification results respectively, so that the role identification efficiency can be improved under the condition of ensuring the accuracy of role identification. Therefore, the service role recognition device provided by the invention can solve the problem that the efficiency of service role recognition is not high enough.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a service role identification method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a service role identification program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., executing a service role identification program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a service character recognition program, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are commonly used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The service role recognition program stored in the memory 11 of the electronic device 1 is a combination of instructions, and when running in the processor 10, can realize:
acquiring a service data set, and classifying data sources of the service data set to obtain a plurality of service subsets from different sources;
extracting a plurality of service subsets which accord with preset source conditions from the service subsets as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
when the role identification result indicates that the target service user corresponding to the target subset is a first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role;
when the role identification result is that the target service user corresponding to the target subset is a second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields;
and performing role segmentation on the target service user according to the keywords to obtain a role corresponding to the target service user.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring a service data set, and classifying data sources of the service data set to obtain a plurality of service subsets from different sources;
extracting a plurality of service subsets which accord with preset source conditions from the service subsets to serve as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
when the role identification result indicates that the target service user corresponding to the target subset is a first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role;
when the role identification result is that the target service user corresponding to the target subset is a second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields;
and performing role segmentation on the target service user according to the keywords to obtain a role corresponding to the target service user.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for identifying a service role is characterized in that the method comprises the following steps:
acquiring a service data set, and classifying data sources of the service data set to obtain a plurality of service subsets from different sources;
extracting a plurality of service subsets which accord with preset source conditions from the service subsets to serve as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
when the role identification result indicates that the target service user corresponding to the target subset is a first role, performing role verification on the target service user, and when the role verification is passed, determining the target service user as the first role;
when the role identification result is that the target service user corresponding to the target subset is a second role, extracting a plurality of target fields in the target subset, and extracting keywords from the plurality of target fields;
and performing role segmentation on the target service user according to the keywords to obtain a role corresponding to the target service user.
2. The method for identifying service roles as claimed in claim 1, wherein the classifying the service data sets into data sources to obtain service subsets from a plurality of different sources comprises:
standardizing the service data set to obtain a standard data set;
performing source identification on the standard data set by using a pre-trained source identification model to obtain sources corresponding to a plurality of standard data in the standard data set;
and clustering the data with the same source to obtain a plurality of service subsets with different sources.
3. The method for identifying a service role according to claim 2, wherein the step of normalizing the service data set to obtain a standard data set comprises:
performing data cleaning on the service data in the service data set to obtain a cleaning data set;
and reserving the data which accords with the preset configuration rule in the cleaning data set as a standard data set.
4. The method for identifying service roles as claimed in claim 1, wherein the step of performing role segmentation on the target service user according to the keyword to obtain the role corresponding to the target service user comprises:
performing keyword label marking on the target service user based on the keyword;
and searching a key role corresponding to the keyword label in a preset keyword role library, and taking the key role as a role corresponding to the target service user.
5. The method for identifying service roles as claimed in claim 1, wherein the extracting the keywords from the plurality of target fields comprises:
collecting a training data set and constructing a deep neural network;
training the deep neural network by using the training data set to obtain a trained keyword extraction model;
and outputting the target fields to the keyword extraction model to obtain keywords corresponding to a plurality of target fields.
6. The method for service role identification according to claim 1, wherein the role verification of the target service user comprises:
acquiring corresponding role information in a preset role information reference library based on the first role;
and performing information verification on the target service user according to the role information, and determining the target service user as a first role when the information verification is passed.
7. The method for service role identification according to any one of claims 1 to 6, wherein the performing initial role identification on the target subset to obtain a role identification result comprises:
comparing the target subset with data in a preset personal information base, and taking a personal role corresponding to the data consistent with the target subset as an initial role of a target service user corresponding to the target subset;
and performing initial verification on the target subset according to the related information of the initial role, and determining the initial role as the role corresponding to the target service user corresponding to the target subset when the initial verification is passed.
8. A service role recognition apparatus, characterized in that the apparatus comprises:
the source classification module is used for acquiring a service data set, and performing data source classification on the service data set to obtain a plurality of service subsets from different sources;
the initial identification module is used for extracting a plurality of service subsets which accord with a preset source condition from the service subsets to serve as target subsets, and performing initial role identification on the target subsets to obtain role identification results;
the role verification module is used for performing role verification on the target service user when the role identification result indicates that the target service user corresponding to the target subset is a first role, and determining the target service user as the first role when the role verification is passed;
and the role subdivision module is used for extracting a plurality of target fields in the target subset, extracting keywords from the target fields and carrying out role subdivision on the target service user according to the keywords to obtain the role corresponding to the target service user when the role identification result shows that the target service user corresponding to the target subset is the second role.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the business role identification method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the service role identification method according to any one of claims 1 to 7.
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| CN116955456A (en) * | 2023-07-21 | 2023-10-27 | 广州拓尔思大数据有限公司 | Public opinion data processing method and system based on open source information |
| CN116955456B (en) * | 2023-07-21 | 2024-02-09 | 广州拓尔思大数据有限公司 | Public opinion data processing method and system based on open source information |
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