CN115293603A - Task allocation method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a task allocation method, which comprises the following steps: acquiring personnel information of service personnel, and generating an information label according to the personnel information; performing function classification on the information labels to obtain classification labels of the information labels; generating a personnel portrait of the service personnel by utilizing the classification label; acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface; acquiring client information of a target client, and performing feature extraction on the client information to obtain client features; and acquiring feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information. In addition, the invention also relates to a block chain technology, and the data list can be stored in the node of the block chain. The invention also provides a task allocation device, electronic equipment and a storage medium. The invention can improve the dispatching efficiency.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a task allocation method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the rapid popularization of the internet, various industries are in network configuration, and the online service industry has a strong development trend. The house leasing market is built into an online service platform, and managers, service personnel and tenants are gathered together. Each tenant has an administrator and a business person, and the administrator needs to assign tasks to the business person for processing, which involves dispatching.
Nowadays, when a new task is available, a manager needs to manually assign work to service staff for processing, but the situation that work assignment is uneven or unreasonable cannot be avoided, so that contradictions may be generated between the manager and the service staff, even unnecessary contradictions may be generated between the service staff due to work assignment problems, and the production efficiency of an enterprise is influenced. Therefore, how to improve the dispatching efficiency becomes a problem to be solved urgently.
Disclosure of Invention
The invention provides a task allocation method, a task allocation device and a computer readable storage medium, and mainly aims to solve the problem of low efficiency in dispatching.
In order to achieve the above object, a task allocation method provided by the present invention includes:
acquiring personnel information of service personnel, and generating an information label according to the personnel information;
performing functional classification on the information labels to obtain classification labels of the information labels;
generating a person representation of the business person using the classification tag;
acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface;
acquiring client information of a target client, and performing feature extraction on the client information to obtain client features;
and acquiring feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information.
Optionally, the generating an information tag according to the personnel information includes:
generating an information text according to the personnel information, and performing word segmentation processing on a text sentence of the information text to obtain a text word corresponding to the text sentence;
inputting the text participles into a preset entity recognition model for entity recognition to obtain entities contained in the information text;
and inputting the information text and the entity into a preset label generation model for label identification to obtain an information label of the information text.
Optionally, the inputting the information text and the entity into a preset tag generation model for tag identification to obtain the information tag of the information text includes:
determining a first embedded vector corresponding to each character in the information text and a second embedded vector corresponding to each character in the entity;
transversely splicing the first embedded vector and the second embedded vector to obtain a spliced vector;
inputting the spliced vector into a preset natural language model for semantic information extraction to obtain a semantic information vector corresponding to the information text;
and inputting the semantic information vector into a preset label generation model for label identification to obtain an information label of the information text.
Optionally, the performing the function classification on the information tag to obtain a classification tag of the information tag includes:
acquiring keywords of the information labels, and coding the keywords one by one to obtain keyword vectors;
coding the context of the keyword to obtain a context vector, and averaging the context word vector to obtain a context vector;
inputting the keyword vector and the context vector into a preset objective function to obtain an optimized vector;
and classifying the optimized vectors by using a preset classification model to obtain the classification labels of the information labels.
Optionally, the generating a person representation of the business person using the category label includes:
generating a unique key value of the classification label to obtain an incidence relation between the classification label and the unique key value;
and acquiring the employee number in the classification label, and writing the association relationship into a preset index according to the employee number to generate the personnel portrait of the business personnel.
Optionally, the configuring the initial frame according to the person image to obtain a dispatching interface includes:
configuring the working time of the initial frame to obtain a primary frame;
performing language configuration on the primary framework to obtain a secondary framework;
and carrying out satisfaction configuration on the secondary framework to obtain a dispatching interface.
Optionally, the completing the distribution of service personnel according to the customer characteristics and the feedback information includes:
performing characteristic analysis on the feedback information to obtain feedback characteristics of the target client;
collecting the customer characteristics and the feedback characteristics into customer wishes, and matching the person portrait with the customer wishes one by one;
and selecting the personnel portrait with the maximum matching degree as a target personnel portrait, and pushing the service personnel to the target customer according to the target personnel portrait.
In order to solve the above problem, the present invention also provides a task assigning apparatus, including:
the information label module is used for acquiring personnel information of the service personnel and generating an information label according to the personnel information
The classification label module is used for carrying out function classification on the information labels to obtain classification labels of the information labels;
the personnel portrait module is used for generating personnel portrait of the business personnel by utilizing the classification labels;
the dispatching interface module is used for acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel picture to obtain a dispatching interface;
the client characteristic module is used for acquiring client information of a target client and extracting characteristics of the client information to obtain client characteristics;
and the personnel allocation module is used for acquiring feedback information of the dispatching interface and completing allocation of service personnel according to the customer characteristics and the feedback information.
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 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 task allocation method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the task assigning method described above.
According to the embodiment of the invention, through acquiring the personnel information of the service personnel and generating the information labels of the personnel information, the basic characteristics of the service personnel can be rapidly obtained, the information labels are subjected to functional classification, the personnel figures of the service personnel are obtained, information comparison can be carried out on different service personnel, the position advantages of the service personnel and the target client which the service personnel is more suitable for are analyzed, the client requirements are known according to the extracted client characteristics and the feedback information obtained according to the dispatching interface, and blind service personnel recommendation is avoided. Therefore, the invention provides a task allocation method, a task allocation device, electronic equipment and a computer readable storage medium, which can solve the problem of low efficiency in dispatching.
Drawings
Fig. 1 is a schematic flowchart of a task allocation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of acquiring an information tag according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of obtaining a category label according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a task assigning apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the task allocation 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 task allocation method. The execution subject of the task allocation 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 task allocation 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 task allocation method according to an embodiment of the present invention.
In this embodiment, the task allocation method includes:
s1, obtaining personnel information of business personnel, and generating an information label according to the personnel information.
In the embodiment of the present invention, the staff information of the service staff includes: name, job number, post, gender, nationality, birthday, city, date of employment, work mailbox, personal mailbox, work time, etc.
In detail, the information tag of the service personnel is generated for the purpose of performing functional classification on the service personnel subsequently.
In an embodiment of the present invention, as described with reference to fig. 2, the generating an information tag according to the person information includes:
s21, generating an information text according to the personnel information, and performing word segmentation processing on a text sentence of the information text to obtain a text word corresponding to the text sentence;
s22, inputting the text word segmentation into a preset entity recognition model for entity recognition to obtain an entity contained in the information text;
s23, inputting the information text and the entity into a preset label generation model for label identification to obtain an information label of the information text.
In detail, the text sentence may be segmented by using a pre-trained artificial intelligence Model with a segmentation function, so as to obtain the text segmentation, where the artificial intelligence Model includes, but is not limited to, an NLP (Natural Language Processing) Model, and an HMM (Hidden Markov Model).
In detail, the preset entity recognition model comprises a first recurrent neural network and a second recurrent neural network, and the text participles are input into the first recurrent neural network according to the sequence of the text participles in the information text for feature extraction, so that a first feature vector corresponding to the text participles is obtained; inputting the text participles into a second recurrent neural network according to the sequence reverse order of the text participles in the information text for feature extraction to obtain a second feature vector corresponding to the text participles; merging the first feature vector and the second feature vector to obtain a merged feature vector, and determining an entity category corresponding to the text word segmentation according to the merged feature vector; and determining the entity contained in the information text based on the entity category.
Further, the first recurrent neural network and the second recurrent neural network are both artificial neural networks which have a tree-like hierarchical structure and in which the network nodes recur the input information according to their connection order.
In this embodiment of the present invention, the inputting the information text and the entity into a preset tag generation model for tag identification to obtain the information tag of the information text includes: determining that each character in the information text corresponds to a first embedded vector and each character in the entity corresponds to a second embedded vector; transversely splicing the first embedded vector and the second embedded vector to obtain a spliced vector; inputting the spliced vector into a preset natural language model for semantic information extraction to obtain a semantic information vector corresponding to the information text; and inputting the semantic information vector into a preset label generation model for label identification to obtain an information label of the information text.
In detail, the preset tag generation model is a model that calculates the information text and the entity by using a tag algorithm and outputs a value representing the information text and the entity.
Further, a pre-trained word vector model may be utilized to determine a first embedding vector corresponding to each character in the information text and a second embedding vector corresponding to each character in the entity, wherein the word vector model includes, but is not limited to, a word2vec model, a bert model.
In detail, the preset natural language model is a preset encoder, and the preset encoder includes an attention layer and a feedforward neural network layer.
In detail, the generation of the stitching vector can refer to the following examples: -the first embedding vector a: (2,3,5) the second embedded vector B: (0,0,3) to obtain a splicing vector C: (2,3,5,0,0,3).
And S2, performing function classification on the information labels to obtain classification labels of the information labels.
In the embodiment of the invention, the classification label comprises job branches, scheduling time and languages mastered by service personnel; the job branch is the job name of the business personnel assigned for different personnel to handle different businesses, such as: the method comprises the following steps that (1) order receiving personnel, experimenters, report compiling personnel and report auditing personnel exist in a detection company, and the experimenters can be subdivided into physical experimenters and chemical experimenters; the shift scheduling time comprises an early shift, a late shift and the like, and can be divided more finely, for example: 7:00-8:00,8:00-9:00; the language is like division of mandarin or dialect, division of chinese and foreign languages.
In detail, the information labels are divided to prepare for configuring the dispatching interface, and what abilities exist in business personnel must be known, and the abilities are collected and classified, so that the abilities can be better displayed on the interface to meet the needs of customers.
In an embodiment of the present invention, as described with reference to fig. 3, the performing function classification on the information tag to obtain a classification tag of the information tag includes:
s31, obtaining keywords of the information labels, and coding the keywords one by one to obtain keyword vectors;
s32, coding the context of the keyword to obtain a context vector, and averaging the context word vector to obtain a context vector;
s33, inputting the keyword vector and the context vector into a preset objective function to obtain an optimized vector;
and S34, classifying the optimized vectors by using a preset classification model to obtain the classification labels of the information labels.
In detail, the extracting of the keywords of the information tag means that a sentence is subjected to word segmentation to obtain words, for example: the 'I likes the subject of mathematics', can be processed into 'I', 'like', 'mathematics', 'this subject', and the processed words are used as key words.
In detail, the objective function may be Y = f (m, n), where m is the keyword vector, n is the context vector, and Y is an optimization vector.
In the embodiment of the present invention, the classification model may include, but is not limited to, a Transfomer model, a Context2vec model, and a CBOW model, and the information tag may be classified by using these models. For example: from seven to nine business personnel in the morning and proficient English and French of the business personnel A and eight to ten business personnel in the morning, strong affinity is obtained from two sentences of words of 'working time', 'business personnel character' and 'good language', the classification label of the information label is classified by using the model.
And S3, generating a personnel portrait of the business personnel by using the classification label.
In the embodiment of the invention, the person representation can comprise a service person personal attribute and a service person capability attribute; the personal attributes of the business personnel comprise sex, age, position, job level, entrance years and income; the business person competency attributes include, but are not limited to, competency assessment results (including high-score ability, low-score ability), stress resistance, toughness, affinity, foreign language levels, and the like.
In detail, the classification label can be displayed by utilizing a mesh structure to obtain the personnel image of the service personnel, so that the searching is convenient.
In an embodiment of the present invention, the generating a person representation of the business person by using the classification tag includes: generating a unique key value of the classification label to obtain an incidence relation between the classification label and the unique key value; and acquiring the employee number in the classification label, and writing the association relationship into a preset index according to the employee number to generate the personnel portrait of the business personnel.
In detail, the unique key value is an index type commonly used in database design, and is mainly used for constraining data and not allowing duplicate key value records to occur, for example: the "F1" key corresponds to a personality label, pressing the "F1" key presents a series of personality options that may be displayed as: strong career, strong responsibility, good mental state, being in charge, afraid of suffering from bitterness, working diligence, doing thoroughness and strong affinity.
In detail, the employee number in the category label refers to, if: the employee number of "Li Hua" is "001", the personality label of "Li Hua" is "seriously responsible", then "001" and "seriously responsible" are corresponding, and "Li Hua" can be found from the personality label of "seriously responsible"; the employee number of "Liu Mei" is "002", the personality label of "Liu Mei" is "strong affinity", then "002" and "strong affinity" are corresponding, and "Liu Mei" can be found from the personality label of "strong affinity".
And S4, obtaining an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface.
In the embodiment of the present invention, the configuration of the initial framework is completed on the SAAS platform, which is a platform for operating SAAS software, and the SAAS software provider provides all network infrastructure, software, and hardware for the target customer, and is responsible for a series of services such as all previous implementation and later maintenance, for example: the network infrastructure comprises modules such as a database, a virtual IP, a storage packet and a cloud host. In detail, since different departments develop different products, when it is known which department the business person belongs to, it can be determined according to the person profile of the business person, for example: when it is determined that the business person belongs to the risk control part, a product of the risk control part may be selected to configure the initial frame.
In the embodiment of the invention, the dispatching interface with completed configuration can be obtained to facilitate the selection of the characteristics of the client. For example: the customer selects 'strong affinity' under the 'character label' index and 'Anhui dialect' under the 'good language' index, so that the requirement of the target customer can be obtained, and the appropriate business personnel can be recommended accurately.
In this embodiment of the present invention, the configuring the initial frame according to the person image to obtain a dispatching interface includes: configuring the working time of the initial frame to obtain a primary frame; performing language configuration on the primary framework to obtain a secondary framework; and carrying out satisfaction configuration on the secondary framework to obtain a dispatching interface.
In detail, the working time configuration of the initial frame determines which business personnel are working, and meanwhile, the system automatically calculates the number of all current customer service personnel to serve, and assigns the agent with the least number of current service personnel to serve, so that the workload of customer service is basically balanced.
In detail, the language configuration of the primary framework is to preferentially match business personnel capable of understanding the language of the customer for service, so that the influence of language communication barrier between the customer and the business personnel on the quality of service is prevented, and the satisfaction degree of the customer is improved.
Furthermore, the satisfaction degree configuration of the secondary framework is performed to preferentially select service personnel served at the previous time to perform dispatching, the customer enters the line again and is preferentially dispatched to the customer service which has served the customer at the previous time, so that the communication cost between the customer and the customer service quality inspection can be reduced, the customer does not need to repeatedly explain the content which has passed through the channel, better experience is provided for the customer, and the satisfaction degree of the customer is improved.
And S5, obtaining the client information of the target client, and performing feature extraction on the client information to obtain the client features.
In this embodiment of the present invention, the performing feature extraction on the customer information to obtain customer features includes: performing word segmentation processing on the client information to obtain client word segmentation; carrying out vector conversion on the client word segmentation to obtain a client vector; and extracting the client semantics of the client vector, and performing feature classification on the client semantics to obtain client features.
In detail, the method for extracting the features of the client information is basically the same as the method for generating the person representation of the service person, and will not be described in detail here.
And S6, obtaining feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information.
In this embodiment of the present invention, the feedback information is a check on a specific requirement of the target customer on a dispatching interface, for example: the target client needs service personnel to solve the doubt for the first use, and needs the service personnel who can understand French and has strong affinity.
In this embodiment of the present invention, the completing the distribution of service personnel according to the customer characteristics and the feedback information includes: performing characteristic analysis on the feedback information to obtain feedback characteristics of the target client; collecting the client characteristics and the feedback characteristics as client wishes, and matching the person pictures with the client wishes one by one; and selecting the personnel portrait with the maximum matching degree as a target personnel portrait, and pushing the service personnel to the target customer according to the target personnel portrait.
In detail, when the target customer needs service personnel to resolve confusion for the first use and needs a service personnel with strong affinity and capable of understanding french, the target customer can be known to be a new customer, the customer will be the service personnel with strong affinity and the intention of the new customer is the service personnel with strong affinity, and then the service personnel can be selected from the personnel portrait, the serial number corresponding to the label can be obtained, and the service personnel corresponding to the serial number can be dispatched to the target customer.
According to the embodiment of the invention, through acquiring the personnel information of the service personnel and generating the information labels of the personnel information, the basic characteristics of the service personnel can be rapidly obtained, the information labels are subjected to functional classification, the personnel figures of the service personnel are obtained, information comparison can be carried out on different service personnel, the position advantages of the service personnel and the target client which the service personnel is more suitable for are analyzed, the client requirements are known according to the extracted client characteristics and the feedback information obtained according to the dispatching interface, and blind service personnel recommendation is avoided. Therefore, the task allocation method provided by the invention can solve the problem of low efficiency in dispatching.
Fig. 4 is a functional block diagram of a task assigning apparatus according to an embodiment of the present invention.
The task assigning apparatus 100 according to the present invention may be installed in an electronic device. Depending on the functionality implemented, the task assignment device 100 may include an information labeling module 101, a category labeling module 102, a people representation module 103, a dispatch interface module 104, a customer characteristics module 105, and a people assignment module 106. 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 information label module is used for acquiring personnel information of business personnel and generating an information label according to the personnel information;
the classification label module is used for performing function classification on the information labels to obtain classification labels of the information labels;
the personnel portrait module is used for generating personnel portrait of the business personnel by utilizing the classification labels;
the dispatching interface module is used for acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel pictures to obtain a dispatching interface;
the client characteristic module is used for acquiring client information of a target client and extracting characteristics of the client information to obtain client characteristics;
and the personnel distribution module is used for acquiring the feedback information of the dispatching interface and completing the distribution of service personnel according to the customer characteristics and the feedback information.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a task allocation 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 task allocation program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., executing task allocation programs, 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, which are 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 task assigning program, etc., but also to temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, 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 task allocation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can implement:
acquiring personnel information of service personnel, and generating an information label according to the personnel information;
performing functional classification on the information labels to obtain classification labels of the information labels;
generating a personnel portrait of the service personnel by utilizing the classification label;
acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface;
acquiring client information of a target client, and performing feature extraction on the client information to obtain client features;
and acquiring feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information.
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 personnel information of service personnel, and generating an information label according to the personnel information;
performing functional classification on the information labels to obtain classification labels of the information labels;
generating a personnel portrait of the service personnel by utilizing the classification label;
acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface;
acquiring client information of a target client, and performing feature extraction on the client information to obtain client features;
and acquiring feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information.
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 task allocation, the method comprising:
acquiring personnel information of service personnel, and generating an information label according to the personnel information;
performing function classification on the information labels to obtain classification labels of the information labels;
generating a person representation of the business person using the classification tag;
acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel image to obtain the dispatching interface;
the method comprises the steps of obtaining client information of a target client, and performing feature extraction on the client information to obtain client features;
and acquiring feedback information of the dispatching interface, and completing the distribution of service personnel according to the customer characteristics and the feedback information.
2. The task assignment method of claim 1, wherein the generating an information label from the person information comprises:
generating an information text according to the personnel information, and performing word segmentation processing on a text sentence of the information text to obtain a text word corresponding to the text sentence;
inputting the text participles into a preset entity recognition model for entity recognition to obtain entities contained in the information text;
and inputting the information text and the entity into a preset label generation model for label identification to obtain an information label of the information text.
3. The task allocation method of claim 2, wherein the step of inputting the information text and the entity into a preset tag generation model for tag recognition to obtain the information tag of the information text comprises:
determining a first embedded vector corresponding to each character in the information text and a second embedded vector corresponding to each character in the entity;
transversely splicing the first embedded vector and the second embedded vector to obtain a spliced vector;
inputting the spliced vector into a preset natural language model for semantic information extraction to obtain a semantic information vector corresponding to the information text;
and inputting the semantic information vector into a preset label generation model for label identification to obtain an information label of the information text.
4. The task assignment method of claim 1, wherein the classifying the information tags into function classes to obtain class tags of the information tags comprises:
acquiring keywords of the information labels, and coding the keywords one by one to obtain keyword vectors;
coding the context of the key words to obtain context vectors, and averaging the context word vectors to obtain context vectors;
inputting the keyword vector and the context vector into a preset objective function to obtain an optimized vector;
and classifying the optimized vectors by using a preset classification model to obtain the classification labels of the information labels.
5. The task assignment method of claim 1, wherein said generating a person representation of the business person using the category label comprises:
generating a unique key value of the classification label to obtain an incidence relation between the classification label and the unique key value;
and acquiring the employee number in the classification label, and writing the association relationship into a preset index according to the employee number to generate the personnel portrait of the business personnel.
6. The task allocation method of claim 1, wherein the configuring the initial frame according to the human drawing to obtain a dispatch interface comprises:
configuring the working time of the initial frame to obtain a primary frame;
performing language configuration on the primary framework to obtain a secondary framework;
and carrying out satisfaction configuration on the secondary framework to obtain a dispatching interface.
7. The task assignment method according to any one of claims 1 to 6, wherein the completing assignment of business personnel according to the customer characteristics and the feedback information comprises:
performing characteristic analysis on the feedback information to obtain feedback characteristics of the target client;
collecting the customer characteristics and the feedback characteristics into customer wishes, and matching the person portrait with the customer wishes one by one;
and selecting the personnel portrait with the maximum matching degree as a target personnel portrait, and pushing the service personnel to the target customer according to the target personnel portrait.
8. A task assigning apparatus, characterized in that the apparatus comprises:
the information label module is used for acquiring personnel information of business personnel and generating an information label according to the personnel information;
the classification label module is used for carrying out function classification on the information labels to obtain classification labels of the information labels;
the personnel portrait module is used for generating personnel portrait of the business personnel by utilizing the classification labels;
the dispatching interface module is used for acquiring an initial frame of a dispatching interface, and configuring the initial frame according to the personnel picture to obtain a dispatching interface;
the client characteristic module is used for acquiring client information of a target client and extracting characteristics of the client information to obtain client characteristics;
and the personnel distribution module is used for acquiring the feedback information of the dispatching interface and completing the distribution of service personnel according to the customer characteristics and the feedback information.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of task allocation according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a task allocation method according to any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730812A (en) * | 2023-01-10 | 2023-03-03 | 中关村科学城城市大脑股份有限公司 | Index information generation method and device, electronic equipment and readable medium |
| CN115934154A (en) * | 2022-12-02 | 2023-04-07 | 武汉昊阳科技有限公司 | Large service data resource allocation management method, device and equipment for digital product |
| CN116361538A (en) * | 2022-11-28 | 2023-06-30 | 中国电力科学研究院有限公司 | Browser-based enterprise hotspot information directional pushing method and system |
| CN116703129A (en) * | 2023-08-07 | 2023-09-05 | 匠达(苏州)科技有限公司 | Intelligent task matching scheduling method and system based on personnel data image |
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- 2022-08-11 CN CN202210961852.1A patent/CN115293603A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116361538A (en) * | 2022-11-28 | 2023-06-30 | 中国电力科学研究院有限公司 | Browser-based enterprise hotspot information directional pushing method and system |
| CN115934154A (en) * | 2022-12-02 | 2023-04-07 | 武汉昊阳科技有限公司 | Large service data resource allocation management method, device and equipment for digital product |
| CN115730812A (en) * | 2023-01-10 | 2023-03-03 | 中关村科学城城市大脑股份有限公司 | Index information generation method and device, electronic equipment and readable medium |
| CN116703129A (en) * | 2023-08-07 | 2023-09-05 | 匠达(苏州)科技有限公司 | Intelligent task matching scheduling method and system based on personnel data image |
| CN116703129B (en) * | 2023-08-07 | 2023-10-24 | 匠达(苏州)科技有限公司 | Intelligent task matching scheduling method and system based on personnel data image |
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