CN115239185A

CN115239185A - Service provider distribution method, service provider distribution device, computer equipment and storage medium

Info

Publication number: CN115239185A
Application number: CN202210958673.2A
Authority: CN
Inventors: 严祖辉
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-10-25

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a distribution method of a service provider, which comprises the following steps: judging whether a service distribution request is received or not; if yes, analyzing target service scene information from the service distribution request; acquiring a target decision network corresponding to target service scene information; receiving a strategy expected value, an entry parameter corresponding to the condition factor and a service provider parameter corresponding to a service provider; carrying out strategy solution on the target decision network based on the parameter values to obtain the optimal solution of the target strategy state node corresponding to the strategy expected value; and determining a target service provider based on the optimal solution and the service provider parameters, and distributing the target service provider to a target service scene corresponding to the target service scene information. The application also provides a distribution device, computer equipment and a storage medium of the service provider. In addition, the present application also relates to blockchain techniques, in which a target decision network may be stored. The method and the device improve the processing efficiency and accuracy of service provider distribution.

Description

Service provider distribution method, service provider distribution device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for distributing service providers, a computer device, and a storage medium.

Background

With the rapid development of the online shopping mall, the number and the number of service providers providing services for the online shopping mall increase, and for each different service scene requirement, a service provider most suitable for the service scene requirement is usually determined from a plurality of service providers and distributed, so as to improve the reasonable utilization of the service provider resources and improve the operation stability of the online shopping mall. The existing method for distributing service providers usually distributes service providers for different service scene requirements manually based on experience, and such a processing method needs to consume more human resources and time resources, has low processing efficiency, and cannot ensure the accuracy of the distributed service providers.

Disclosure of Invention

An object of the embodiments of the present application is to provide a method and an apparatus for allocating service providers, a computer device, and a storage medium, so as to solve the technical problems that the existing method for allocating service providers usually manually allocates service providers for different service scene requirements based on experience, such a processing method needs to consume more human resources and time resources, has low processing efficiency, and cannot ensure the accuracy of the allocated service providers.

In order to solve the foregoing technical problem, an embodiment of the present application provides a distribution method for a service provider, which adopts the following technical solutions:

judging whether a service distribution request input by a user is received or not; the service distribution request carries target service scene information;

if yes, analyzing the target service scene information from the service distribution request;

acquiring a target decision network corresponding to the target service scene information from a preset decision network template library; the target decision network is constructed and generated based on preset condition factors and preset service providers;

receiving a strategy expected value input by the user, an entry parameter corresponding to the condition factor and a service provider parameter corresponding to the service provider;

carrying out strategy solution on the objective decision network based on the access parameter to obtain the optimal solution of an objective strategy state node corresponding to the strategy expected value;

and determining a target service provider corresponding to the service distribution request based on the optimal solution and the service provider parameters, and distributing the target service provider to a target service scene corresponding to the target service scene information.

Further, before the step of obtaining the objective decision network corresponding to the objective service scenario information from a preset decision network template library, the method further includes:

acquiring a condition factor corresponding to the target service scene information from a preset condition factor library;

acquiring a service provider corresponding to the target service scene information from a preset service provider database;

taking the condition factor as a decision network strategy factor, and constructing a decision tree template by using the decision network strategy factor;

taking the service provider as a strategy state node of the decision tree template to obtain the target decision network;

storing the objective decision network in the decision network template base.

Further, the step of constructing a decision tree template by using the conditional factors as decision network policy factors and using the decision network policy factors specifically includes:

freely combining the decision network strategy factors to obtain a plurality of factor combinations;

generating corresponding intermediate state nodes based on the factor combinations;

constructing the decision tree template based on the intermediate state nodes.

Further, before the step of obtaining the condition factor corresponding to the target service scenario information from a preset condition factor library, the method further includes:

judging whether a factor configuration request input by a target user is received; wherein, the factor configuration request carries factor data and user information of the target user;

if a factor configuration request input by the target user is received, analyzing the user information from the factor configuration request;

performing authority verification on the target user based on the user information;

if the authority verification is passed, the factor data carried in the factor configuration request is obtained;

and storing the factor data in a preset first database to obtain the conditional factor database.

Further, the step of performing the authorization verification on the target user based on the user information specifically includes:

calling a preset authority verification model;

determining a user role category corresponding to the user information through the authority verification model, and determining a target authority score corresponding to the user role category based on a preset corresponding relation between the role category and the authority score;

inquiring a processing authority score corresponding to the service operation configured by the factor from a preset service operation authority table;

judging whether the target permission score is greater than the processing permission score;

and if the processing authority score is larger than the processing authority score, judging that the authority verification is passed, otherwise, judging that the authority verification is not passed.

Further, the step of determining a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter specifically includes:

obtaining the number of the optimal solutions;

if the number of the optimal solutions is 1, acquiring a first service provider corresponding to the optimal solutions, and taking the first service provider as the target service provider;

if the number of the optimal solutions is multiple, determining a weight parameter of a second service provider corresponding to each optimal solution based on the service provider parameter;

based on each weight parameter, determining a third service provider from all the second service providers by using a preset random weight algorithm;

and taking the third service provider as the target service provider.

acquiring various types of service scene information;

acquiring a pre-constructed decision network;

acquiring matching information corresponding to the service scene information and the decision network;

establishing a one-to-one corresponding incidence relation between each service scene information and each decision network based on the matching information;

and storing all the decision networks in a preset second database based on the incidence relation to obtain the decision network template base.

In order to solve the above technical problem, an embodiment of the present application further provides an allocation apparatus for a service provider, which adopts the following technical solutions:

the first judgment module is used for judging whether a service distribution request input by a user is received or not; the service distribution request carries target service scene information;

the first analysis module is used for analyzing the target service scene information from the service distribution request if the target service scene information is the service distribution request;

the first acquisition module is used for acquiring a target decision network corresponding to the target service scene information from a preset decision network template library; the target decision network is constructed and generated based on preset condition factors and preset service providers;

the receiving module is used for receiving the strategy expected value input by the user, the parameter value corresponding to the condition factor and the service provider parameter corresponding to the service provider;

the processing module is used for carrying out strategy solution on the target decision network based on the input parameter to obtain the optimal solution of a target strategy state node corresponding to the strategy expected value;

and the first determining module is used for determining a target service provider corresponding to the service distribution request based on the optimal solution and the service provider parameters and distributing the target service provider to a target service scene corresponding to the target service scene information.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

receiving a strategy expected value input by the user, an entry parameter value corresponding to the condition factor and a service provider parameter corresponding to the service provider;

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

the embodiment of the application receives a service allocation request input by a user. Analyzing target service scene information from a service allocation request, then obtaining a target decision network corresponding to the target service scene information from a preset decision network template base, then receiving a strategy expected value input by a user, an entry parameter corresponding to a condition factor and a service provider parameter corresponding to a service provider, then carrying out strategy solution on the target decision network based on the entry parameter to obtain an optimal solution of a target strategy state node corresponding to the strategy expected value, subsequently determining a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter, and allocating the target service provider to a target service scene corresponding to the target service scene information. According to the method and the system, the target decision network corresponding to the target service scene information is called, and the user can quickly and accurately determine the corresponding target service provider based on the target decision network and use the target service provider for distribution only by inputting the corresponding strategy expected value, the parameter and the service provider parameter, so that the response speed of the service provider response is effectively improved, and the processing efficiency and accuracy of the service provider distribution are improved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a method for facilitator assignment according to the present application;

FIG. 3 is a schematic block diagram of one embodiment of a dispensing device for a facilitator according to the subject application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof in the description and claims of this application and the description of the figures above, are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between

terminal devices

101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the distribution method of the service provider provided in the embodiment of the present application is generally executed by the server/terminal device, and accordingly, the distribution device of the service provider is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of a method for facilitator assignment according to the present application is shown. The distribution method of the service provider comprises the following steps:

step S201, judging whether a service distribution request input by a user is received; and the service distribution request carries target service scene information.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the allocation method of the service provider operates may obtain the service allocation request input by the user through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, an UWB (ultra wideband) connection, and other wireless connection means now known or developed in the future. The service distribution request is request data which is triggered by a user and used for requesting the electronic equipment to distribute corresponding service providers based on the rescue service of the target service scene information. When the rescue is dispatched based on the rescue service, the most suitable service provider is decided from a plurality of service providers for rescue. In addition, the target service scenario information may be name information of a service scenario that needs to be allocated to a service provider.

And step S202, if yes, analyzing the target service scene information from the service distribution request.

In this embodiment, by performing parsing processing on the service allocation request, the target service scenario information carried in the service allocation request may be obtained.

Step S203, acquiring a target decision network corresponding to the target service scene information from a preset decision network template library; the target decision network is constructed and generated based on preset condition factors and preset service providers.

In the present embodiment, the decision network template library may be constructed based on the association relationship between various types of service scenario information and the decision network. The specific implementation process for generating the objective decision network is constructed based on the preset condition factor and the preset service provider, which will be described in further detail in the following specific embodiments and will not be elaborated herein.

Step S204, receiving the strategy expected value input by the user, the parameter value corresponding to the condition factor and the service provider parameter corresponding to the service provider.

In this embodiment, the user may input the policy expected value, the parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider according to the actual service usage requirement. The policy expected value is a state value corresponding to a policy state node in a target decision network, the input parameter is an actual attribute value of a condition factor, and the service provider parameter is a weight parameter of a service provider.

Step S205, performing strategy solution on the target decision network based on the access parameter to obtain an optimal solution of the target strategy state node corresponding to the strategy expected value.

In this embodiment, the intermediate state nodes corresponding to the condition factors in the objective decision network are substituted into the one-to-one corresponding entry parameter values, and then the objective decision network is operated to obtain the optimal solution of the objective policy state nodes meeting the policy expectation value.

Step S206, determining a target service provider corresponding to the service distribution request based on the optimal solution and the service provider parameter, and distributing the target service provider to a target service scene corresponding to the target service scene information.

In this embodiment, the specific implementation process of determining the target facilitator corresponding to the service allocation request based on the optimal solution and the facilitator parameter is described in further detail in the following specific embodiments, and will not be described in detail herein.

The method and the device receive the service distribution request input by the user. Analyzing target service scene information from a service allocation request, then obtaining a target decision network corresponding to the target service scene information from a preset decision network template base, then receiving a strategy expected value input by a user, an entry parameter corresponding to a condition factor and a service provider parameter corresponding to a service provider, then carrying out strategy solution on the target decision network based on the entry parameter to obtain an optimal solution of a target strategy state node corresponding to the strategy expected value, subsequently determining a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter, and allocating the target service provider to a target service scene corresponding to the target service scene information. According to the method and the system, the target decision network corresponding to the target service scene information is called, and the user can quickly and accurately determine the corresponding target service provider based on the target decision network and use the target service provider for distribution only by inputting the corresponding strategy expected value, the parameter and the service provider parameter, so that the response speed of the service provider response is effectively improved, and the processing efficiency and accuracy of the service provider distribution are improved.

In some optional implementations, before step S203, the electronic device may further perform the following steps:

and acquiring a condition factor corresponding to the target service scene information from a preset condition factor library.

In this embodiment, the condition factor library may be a database that is pre-constructed by relevant users according to actual service usage requirements and stores various types of service scenario information and condition factors corresponding to the various service scenario information one to one. The related users can carry out data dynamic configuration in the condition factor library according to self requirements, and the expansibility is strong.

And acquiring a service provider corresponding to the target service scene information from a preset service provider database.

In this embodiment, the service provider database may be a database in which various service provider databases are stored, which is pre-constructed by the relevant user according to the actual service usage requirement. The related users can carry out data dynamic configuration in the database of the service provider according to the self requirements, and the expansibility is strong. The service provider corresponding to the target service scenario information is a provider that can provide a service for a service scenario corresponding to the target service scenario information.

And taking the condition factor as a decision network strategy factor, and constructing a decision tree template by using the decision network strategy factor.

In this embodiment, the specific implementation process of using the condition factor as a decision network policy factor and using the decision network policy factor to construct a decision tree template is described in further detail in the following specific embodiments, and will not be described in detail herein.

And taking the service provider as a strategy state node of the decision tree template to obtain the target decision network.

In this embodiment, after the decision tree template is obtained, the service provider may be further used as a policy state node of the decision tree template to adjust the topology structure of the decision tree template, so as to obtain a final target decision network. The policy state node may also be referred to as a target branch decision node.

Storing the objective decision network in the decision network template base.

According to the method and the system, the condition factors corresponding to the target service scene information and the service providers corresponding to the target service scene information can be quickly acquired based on the preset condition factor database and the service provider database, so that the required target decision network can be quickly established based on the acquired condition factors and the service providers, the target service providers corresponding to the service distribution request can be quickly and accurately determined based on the target decision network, and the response speed of service provider distribution is effectively improved.

In some optional implementation manners of this embodiment, the above-mentioned taking the condition factor as a decision network policy factor and using the decision network policy factor to construct a decision tree template includes the following steps:

and freely combining the decision network strategy factors to obtain a plurality of factor combinations.

In this embodiment, the number of the decision network policy factors includes a plurality of factors, and the free combination refers to combining one or more decision network policy factors, and each obtained factor combination may include one decision network policy factor or include a plurality of decision network policy factors.

Generating a corresponding intermediate state node based on the factor combination.

In this embodiment, after all the decision network policy factors are freely combined to obtain a plurality of factor combinations, an intermediate state node satisfying the factor combinations may be constructed. Wherein the decision tree template may be obtained by computing intermediate states of the factor combinations and using them as intermediate state nodes of the initial decision tree template. In addition, existing decision tree models can be used as the initial decision tree template. For example, if the number of the decision network policy factors (hereinafter, referred to as factors) is 2, including factors a and B, the free combination of the factors a and B can result in three factor combinations, i.e., a first factor combination including factor a, a second factor combination including factor B, and a third factor combination including factor a and factor B. Continuing with the above example, by computing the intermediate states of each factor combination, the corresponding 8 intermediate state nodes can be obtained: intermediate status node 1 (A: TURE, B: NULL), intermediate status node 2 (A: FALSE, B: NULL), intermediate status node 3 (A: NULL, B: TURE), intermediate status node 4 (A: NULL, B: FALSE), intermediate status node 5 (A: TURE, B: TURE), intermediate status node 6 (A: TURE, B: FALSE), intermediate status node 7 (A: FALSE, B: TURE), intermediate status node 8 (A: FALSE, B: FALSE), where TURE indicates a satisfaction factor, FALSE indicates a non-satisfaction factor, and NULL indicates no factor.

Constructing the decision tree template based on the intermediate state nodes.

In this embodiment, the intermediate state node is used as a topology structure of the initial decision tree template to obtain a corresponding decision tree template.

According to the method and the device, the policy factors of the decision network are freely combined to obtain a plurality of factor combinations, and the intermediate state nodes corresponding to the factor combinations are constructed, so that the decision tree template can be rapidly generated based on the intermediate state nodes, and the decision tree template can be used for generating the corresponding target decision network subsequently, so that the target service provider corresponding to the service distribution request can be rapidly and accurately determined based on the target decision network, and the response speed of service provider distribution is effectively improved.

In some optional implementation manners, before the step of obtaining the condition factor corresponding to the target service scenario information from a preset condition factor library, the electronic device may further perform the following steps:

judging whether a factor configuration request input by a target user is received; wherein the factor configuration request carries factor data and user information of the target user.

In this embodiment, the target user may be a configurator related to the condition factor library construction work. The factor user is data for constructing a condition factor library. The user information is identification information for indicating the position identity of the target user, and the user information may include the name of the target user, position information of the target user, and the like.

And if a factor configuration request input by the target user is received, analyzing the user information from the factor configuration request.

In this embodiment, by analyzing the factor configuration request, the corresponding user information can be obtained

And performing authority verification on the target user based on the user information.

In this embodiment, the above specific implementation process of performing the authority verification on the target user based on the user information is further described in detail in the following specific embodiments, and is not set forth herein more extensively.

And if the authority passes the verification, acquiring the factor data carried in the factor configuration request.

In this embodiment, corresponding factor data may be obtained by performing parsing processing on the factor configuration request.

In this embodiment, the first database may be a pre-constructed database that does not store data. In addition, by moving the traditional complicated conditions to the strategy factors, namely factor data, through the hard code calculation logic and storing the strategy factors in the condition factor library, the readability of the codes can be increased, the codes are neat, and the maintenance is easy.

According to the method and the device, when a factor configuration request input by a target user is received, authority verification can be performed on the target user firstly, and a corresponding condition factor library can be established based on factor data input by the target user after the authority verification is passed, so that the dynamic configuration of the factor data can be performed by the target user according to actual use requirements, the expandability is strong, and the use experience of the target user is improved. Therefore, the condition factors corresponding to the target service scene information can be quickly and conveniently acquired subsequently based on the condition factor library, and the generation rate of the target decision network is favorably improved. In addition, only when the user passes the authority verification, the factor configuration request input by the user is subsequently responded, so that the normalization of the factor configuration process is ensured, and adverse consequences caused by responding to the factor configuration request input by an illegal user are avoided.

In some optional implementations, the performing the authorization verification on the target user based on the user information includes the following steps:

and calling a preset authority verification model.

In this embodiment, the authority verification model may specifically be a classification tree model generated by pre-training. Each node except the leaf node in the classification tree model corresponds to one classification rule, and each classification rule classifies one type of data in the user information. Therefore, the classification tree model can classify the user information layer by layer, and finally the user information is distributed to one leaf node. And then, according to the preset corresponding relation between the leaf nodes and the authority scores, the target authority scores corresponding to the user information can be determined.

And determining a user role category corresponding to the user information through the authority verification model, and determining a target authority score corresponding to the user role category based on a preset corresponding relation between the role category and the authority score.

In this embodiment, for example, if the user information includes: "job position: 6, working team: b, developing tasks: 08', if the root node of the classification tree model is classified through the work role class, the second-level node is classified through the work team, and the third-level node is classified through the development task, the user information can be distributed to one leaf node through three-layer classification, and then the target permission score corresponding to the user information can be determined according to the preset corresponding relation between the leaf node and the work team.

And inquiring a processing authority score corresponding to the service operation configured by the factor from a preset service operation authority table.

In this embodiment, a service operation authority table is created in advance, and processing authority scores corresponding to each service operation one to one are stored in the service operation authority table.

And judging whether the target permission score is larger than the processing permission score.

After the user information in the factor configuration request is acquired, the classification tree model is called to quickly acquire the target permission score corresponding to the user information of the target user, and then the target permission score and the processing permission score corresponding to the service operation of the factor configuration are subjected to numerical value to obtain a comparison result, so that whether the target user has the processing permission of the factor configuration can be accurately and quickly judged according to the comparison result. Only when the processing right of the user factor configuration is judged, the factor configuration request input by the user is subsequently responded, the normalization of the factor configuration process is ensured, and adverse consequences caused by responding to the factor configuration request input by an illegal user are avoided.

In some optional implementations of this embodiment, step S206 includes the following steps:

and acquiring the number of the optimal solutions.

In the present embodiment, the number of the optimal solutions may include one or more.

And if the number of the optimal solutions is 1, acquiring a first service provider corresponding to the optimal solutions, and taking the first service provider as the target service provider.

In this embodiment, if the number of the optimal solutions is 1, the service provider corresponding to the optimal solution is directly used as the target service provider to be allocated.

And if the number of the optimal solutions is multiple, determining the weight parameters of the second service provider corresponding to each optimal solution based on the service provider parameters.

In this embodiment, the above-mentioned service provider parameter may be a weight parameter of the service provider that is introduced by the user according to the actual usage requirement. If the optimal solutions are multiple, the weight parameters of the second service providers respectively corresponding to the optimal solutions are obtained, and then the weight parameters of the second service providers are processed according to a random weight algorithm to obtain the final target service provider.

And determining a third service provider from all the second service providers by using a preset random weight algorithm based on each weight parameter.

In this embodiment, the process of determining a third service provider from all the second service providers by using a preset random weight algorithm based on each of the weight parameters may include: calculating the weight proportion of each second service provider based on each weight parameter; calculating the coverage area of each second service provider based on the weight proportion; generating a random number using a random number function; determining a target coverage range matched with the random number from all the coverage ranges; and screening out the service providers corresponding to the target coverage range from all the second service providers as the third service provider. Wherein, the weight ratio of any one second service provider = the weight parameter/total weight of the second service provider, and the total weight is the sum of the weight parameters of all the second service providers. For example, if the second service provider includes service provider 1, service provider 2 and service provider 3, and the weight parameter of service provider 1 is 0.3, the weight parameter of service provider 2 is 0.6 and the weight parameter of service provider 3 is 0.1, the weight ratio of service provider 1 is 0.3/(0.3 +0.6+ 0.1) =3, the weight ratio of service provider 2 is 0.6/(0.3 +0.6+ 0.1) =6, and the weight ratio of service provider 3 is 0.1/(0.3 +0.6+ 0.1) =1 as calculated according to the above data. According to the obtained weight proportion, the coverage area of the service provider 1 is calculated to be (0, 3), the coverage area of the service provider 2 is calculated to be (3, 9), the coverage area of the service provider 3 is calculated to be (9, 10), then a random number function is used, a random number between (0, 10) is taken, and how to select is determined according to which coverage area the random number falls in, if the random number is 6 and is in the coverage area of (3, 9), the service provider 2 is selected as the third service provider.

And taking the third service provider as the target service provider.

After the optimal solution of the target strategy state node corresponding to the strategy expected value is obtained, the final target service provider can be determined by adopting different calculation modes according to the number of the optimal solutions, the intelligence determined by the target service provider is improved, and the accuracy of the obtained target service provider is ensured.

In some optional implementation manners of this embodiment, before step S203, the electronic device may further perform the following steps:

various types of service scenario information are acquired.

In this embodiment, service scenes are divided into multiple types in advance according to actual service requirements, and the service scene information may refer to name information of various service scenes.

And acquiring a pre-constructed decision network.

In this embodiment, for each different type of service scenario, a corresponding decision network is constructed in advance according to the service scenario, and the construction method of the decision network corresponding to each service scenario may refer to the construction method of the target decision network, which will not be described herein too much.

And acquiring matching information corresponding to the service scene information and the decision network.

In this embodiment, the matching information may be information that is input by a relevant user in advance and identifies a matching relationship between the service scenario information and the decision network.

And establishing a one-to-one corresponding incidence relation between each service scene information and each decision network based on the matching information.

And storing all the decision networks in a preset second database based on the incidence relation to obtain the decision network template library.

In this embodiment, the second database may be a pre-constructed database that does not store data. In addition, after the decision network template library is obtained, the decision network template library can be cached in a memory.

According to the method and the system, the decision network template base is constructed based on the incidence relation between various types of service scene information and the decision network, so that the target decision network corresponding to the target service scene information can be quickly and conveniently inquired from the decision network template base according to the target service scene information input by a user, further, the target service provider corresponding to the service distribution request can be quickly and accurately generated based on the target decision network, and the response speed of service provider distribution is effectively improved.

It is emphasized that the policy expectations may also be stored in nodes of a blockchain to further ensure privacy and security of the policy expectations.

The block chain referred by the application 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.

The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware that is configured to be instructed by computer-readable instructions, which can be stored in a computer-readable storage medium, and when executed, the programs may include the processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an allocation apparatus of a service provider, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 3, the distribution device 300 of the service provider according to the present embodiment includes: the device comprises a first judging module 301, a first analyzing module 302, a first obtaining module 303, a receiving module 304, a processing module 305 and a first determining module 306. Wherein:

a first determining module 301, configured to determine whether a service allocation request input by a user is received; the service distribution request carries target service scene information;

a first parsing module 302, configured to parse the target service scenario information from the service allocation request if the target service scenario information is received;

a first obtaining module 303, configured to obtain a target decision network corresponding to the target service scenario information from a preset decision network template library; the target decision network is constructed and generated based on preset condition factors and preset service providers;

a receiving module 304, configured to receive a policy expected value input by the user, an entry parameter value corresponding to the condition factor, and an facilitator parameter corresponding to the facilitator;

the processing module 305 is configured to perform policy solution on the target decision network based on the entry parameter to obtain an optimal solution of a target policy state node corresponding to the policy expected value;

a first determining module 306, configured to determine, based on the optimal solution and the service provider parameter, a target service provider corresponding to the service allocation request, and allocate the target service provider to a target service scenario corresponding to the target service scenario information.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the service provider allocation method in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the distribution device of the service provider further includes:

a second obtaining module, configured to obtain a condition factor corresponding to the target service scenario information from a preset condition factor library;

the third acquisition module is used for acquiring a service provider corresponding to the target service scene information from a preset service provider database;

the construction module is used for taking the condition factors as decision network strategy factors and constructing a decision tree template by using the decision network strategy factors;

the second determining module is used for taking the service provider as a strategy state node of the decision tree template to obtain the target decision network;

and the first storage module is used for storing the target decision network in the decision network template base.

In some optional implementations of this embodiment, the building module includes:

the combination submodule is used for carrying out free combination on the decision network strategy factors to obtain a plurality of factor combinations;

the generating submodule is used for generating a corresponding intermediate state node based on the factor combination;

and the construction submodule is used for constructing the decision tree template based on the intermediate state node.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the distribution method of the service provider in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the allocating apparatus of the service provider further includes:

the second judgment module is used for judging whether a factor configuration request input by a target user is received or not; wherein, the factor configuration request carries factor data and user information of the target user;

the second analysis module is used for analyzing the user information from the factor configuration request if the factor configuration request input by the target user is received;

the verification module is used for performing authority verification on the target user based on the user information;

a fourth obtaining module, configured to obtain the factor data carried in the factor configuration request if the permission verification passes;

and the second storage module is used for storing the factor data in a preset first database to obtain the conditional factor database.

In some optional implementations of this embodiment, the verification module includes:

the calling submodule is used for calling a preset authority verification model;

the first determining submodule is used for determining a user role category corresponding to the user information through the authority verification model and determining a target authority score corresponding to the user role category based on the corresponding relation between the preset role category and the authority score;

the query submodule is used for querying a processing authority score corresponding to the service operation configured by the factor from a preset service operation authority table;

the judgment sub-module is used for judging whether the target authority score is larger than the processing authority score or not;

and the first judgment sub-module is used for judging that the authority verification passes if the processing authority score is larger than the processing authority score, and otherwise, judging that the authority verification fails.

In some optional implementations of this embodiment, the first determining module 306 includes:

an obtaining submodule for obtaining the number of the optimal solutions;

a second determining submodule, configured to, if the number of the optimal solutions is 1, obtain a first service provider corresponding to the optimal solution, and use the first service provider as the target service provider;

a second determining submodule, configured to determine, based on the service provider parameters, weight parameters of a second service provider corresponding to each optimal solution if the number of the optimal solutions is multiple;

a third determining submodule, configured to determine, based on each of the weight parameters, a third facilitator from all the second facilitators by using a preset random weight algorithm;

and the third judgment submodule is used for taking the third service provider as the target service provider.

the fifth acquisition module is used for acquiring various types of service scene information;

a sixth obtaining module, configured to obtain a pre-constructed decision network;

a seventh obtaining module, configured to obtain matching information corresponding to the service scenario information and the decision network;

the association module is used for establishing one-to-one association relationship between each service scene information and each decision network based on the matching information;

and the third storage module is used for storing all the decision networks in a preset second database based on the incidence relation to obtain the decision network template base.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4 in particular, fig. 4 is a block diagram of a basic structure of a computer device according to the embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in 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 computer device 4. Of course, the memory 41 may also include both an internal storage unit of the computer device 4 and an external storage device thereof. In this embodiment, the memory 41 is generally used for storing an operating system and various application software installed in the computer device 4, such as computer readable instructions of a distribution method of a service provider. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the distribution method of the service provider.

The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, after a service allocation request input by a user is received. Analyzing target service scene information from a service allocation request, then acquiring a target decision network corresponding to the target service scene information from a preset decision network template base, then receiving a strategy expected value input by a user, an input parameter corresponding to a condition factor and a service provider parameter corresponding to a service provider, then carrying out strategy solution on the target decision network based on the input parameter to obtain an optimal solution of a target strategy state node corresponding to the strategy expected value, subsequently determining a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter, and allocating the target service provider to a target service scene corresponding to the target service scene information. According to the method and the system, the target decision network corresponding to the target service scene information is called, and the user can quickly and accurately determine the corresponding target service provider based on the target decision network and use the target service provider for distribution only by inputting the corresponding strategy expected value, the parameter and the service provider parameter, so that the response speed of the service provider response is effectively improved, and the processing efficiency and accuracy of the service provider distribution are improved.

The present application further provides another embodiment, which is a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the method for facilitator allocation as described above.

in the embodiment of the application, after a service allocation request input by a user is received. Analyzing target service scene information from a service allocation request, then obtaining a target decision network corresponding to the target service scene information from a preset decision network template base, then receiving a strategy expected value input by a user, an entry parameter corresponding to a condition factor and a service provider parameter corresponding to a service provider, then carrying out strategy solution on the target decision network based on the entry parameter to obtain an optimal solution of a target strategy state node corresponding to the strategy expected value, subsequently determining a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter, and allocating the target service provider to a target service scene corresponding to the target service scene information. According to the method and the system, the target decision network corresponding to the target service scene information is called, and the user can quickly and accurately determine the corresponding target service provider based on the target decision network and use the target service provider for distribution only by inputting the corresponding strategy expected value, the parameter and the service provider parameter, so that the response speed of the service provider response is effectively improved, and the processing efficiency and accuracy of the service provider distribution are improved.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and the embodiments are provided so that this disclosure will be thorough and complete. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields, and all the equivalent structures are within the protection scope of the present application.

Claims

1. A method for distributing service providers, comprising the steps of:

2. The method as claimed in claim 1, further comprising, before the step of obtaining the objective decision network corresponding to the objective service scenario information from a preset decision network template library, the steps of:

storing the objective decision network in the decision network template base.

3. The method according to claim 2, wherein the step of constructing a decision tree template by using the condition factors as decision network policy factors specifically comprises:

freely combining the policy factors of the decision network to obtain a plurality of factor combinations;

constructing the decision tree template based on the intermediate state nodes.

4. The method as claimed in claim 2, further comprising, before the step of obtaining the condition factor corresponding to the target service scenario information from a preset condition factor library, the steps of:

if the authority passes the verification, the factor data carried in the factor configuration request is obtained;

5. The method for distributing service providers according to claim 4, wherein the step of performing the authorization verification on the target user based on the user information specifically comprises:

calling a preset authority verification model;

6. The method for allocating service providers according to claim 1, wherein the step of determining the target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameters specifically comprises:

obtaining the number of the optimal solutions;

and taking the third service provider as the target service provider.

7. The method for distributing service providers according to claim 1, wherein before the step of obtaining the objective decision network corresponding to the objective service scenario information from a preset decision network template library, the method further comprises:

acquiring various types of service scene information;

acquiring a pre-constructed decision network;

8. An apparatus for distributing a service provider, comprising:

the first analysis module is used for analyzing the target service scene information from the service distribution request if the target service scene information exists;

the processing module is used for carrying out strategy solution on the target decision network based on the access parameter to obtain the optimal solution of the target strategy state node corresponding to the strategy expected value;

9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the facilitator's distribution method of any of claims 1 to 7.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, carry out the steps of the method of distribution of a facilitator as claimed in any of claims 1 to 7.