Service provider allocation method and apparatus, computer device, and storage medium

Abstract

This application belongs to the field of artificial intelligence and relates to a service provider allocation method, including: determining whether a service allocation request has been received; if so, parsing target service scenario information from the service allocation request; obtaining a target decision network corresponding to the target service scenario information; receiving a policy expectation value, input parameter values ​​corresponding to condition factors, and service provider parameters corresponding to the service provider; solving the policy on the target decision network based on the input parameter values ​​to obtain the optimal solution of the target policy state node corresponding to the policy expectation value; determining the target service provider based on the optimal solution and the service provider parameters, and allocating the target service provider to the target service scenario corresponding to the target service scenario information. This application also provides a service provider allocation device, computer equipment, and storage medium. Furthermore, this application relates to blockchain technology, and the target decision network can be stored in the blockchain. This application improves the processing efficiency and accuracy of service provider allocation.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to methods, apparatus, computer equipment and storage media for allocating services to service providers. Background Technology

[0002] With the rapid development of online shopping malls, the number of service providers offering services to these malls is also increasing. For each different service scenario, it is typically necessary to select the most suitable service provider from among multiple providers and allocate them accordingly. This is to improve the rational utilization of service provider resources and enhance the stability of the online shopping mall's operation. Currently, the method of allocating service providers is usually done manually based on experience for different service scenarios. This approach consumes significant human and time resources, is inefficient, and cannot guarantee the accuracy of the allocated service providers. Summary of the Invention

[0003] The purpose of this application is to provide a service provider allocation method, apparatus, computer equipment, and storage medium to solve the technical problems that existing service provider allocation methods are usually done manually based on experience for different service scenarios. This method requires a lot of human and time resources, has low processing efficiency, and cannot ensure the accuracy of the allocated service providers.

[0004] To address the aforementioned technical problems, this application provides a method for allocating service providers, employing the following technical solution:

[0005] Determine whether a service allocation request input by the user has been received; wherein the service allocation request carries target service scenario information;

[0006] If so, the target service scenario information is parsed from the service allocation request;

[0007] Obtain the target decision network corresponding to the target service scenario information from the preset decision network template library; wherein, the target decision network is generated based on preset condition factors and preset service providers;

[0008] Receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider;

[0009] Based on the input parameter values, the target decision network is used to solve the policy, and the optimal solution of the target policy state node corresponding to the policy expectation value is obtained.

[0010] Based on the optimal solution and the service provider parameters, the target service provider corresponding to the service allocation request is determined, and the target service provider is allocated to the target service scenario corresponding to the target service scenario information.

[0011] Furthermore, before the step of obtaining the target decision network corresponding to the target service scenario information from the preset decision network template library, the method further includes:

[0012] Obtain the condition factors corresponding to the target service scenario information from the preset condition factor library;

[0013] Retrieve the service provider corresponding to the target service scenario information from the preset service provider database;

[0014] The conditional factors are used as decision network strategy factors, and decision tree templates are constructed using the decision network strategy factors.

[0015] The target decision network is obtained by using the service provider as the policy state node of the decision tree template;

[0016] The target decision network is stored in the decision network template library.

[0017] Furthermore, the step of using the conditional factors as decision network policy factors and constructing a decision tree template using the decision network policy factors specifically includes:

[0018] Multiple factor combinations can be obtained by freely combining the decision network strategy factors;

[0019] Based on the aforementioned factor combinations, corresponding intermediate state nodes are generated;

[0020] The decision tree template is constructed based on the intermediate state nodes.

[0021] Furthermore, before the step of obtaining the condition factors corresponding to the target service scenario information from the preset condition factor library, the method further includes:

[0022] Determine whether a factor configuration request input from the target user has been received; wherein the factor configuration request carries factor data and the user information of the target user;

[0023] If a factor configuration request is received from the target user, the user information is parsed from the factor configuration request;

[0024] The target user's permissions are verified based on the user information.

[0025] If the permission verification is successful, obtain the factor data carried in the factor configuration request;

[0026] The factor data is stored in a preset first database to obtain the condition factor library.

[0027] Furthermore, the step of verifying the permissions of the target user based on the user information specifically includes:

[0028] Invoke the preset permission verification model;

[0029] The user role category corresponding to the user information is determined by the permission verification model, and the target permission score corresponding to the user role category is determined based on the preset correspondence between role category and permission score.

[0030] Retrieve the processing permission score corresponding to the business operation configured by the factor from the preset business operation permission table;

[0031] Determine whether the target permission score is greater than the processing permission score;

[0032] If the score is greater than the processing permission score, the permission verification is considered successful; otherwise, the permission verification is considered unsuccessful.

[0033] Furthermore, 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 includes:

[0034] Obtain the number of the optimal solutions;

[0035] If the number of optimal solutions is 1, obtain the first service provider corresponding to the optimal solution, and use the first service provider as the target service provider;

[0036] If there are multiple optimal solutions, the weight parameters of the second service provider corresponding to each optimal solution are determined based on the service provider parameters.

[0037] Based on the weight parameters, a third service provider is determined from all the second service providers using a preset random weight algorithm;

[0038] The third service provider is designated as the target service provider.

[0039] Furthermore, before the step of obtaining the target decision network corresponding to the target service scenario information from the preset decision network template library, the method further includes:

[0040] Obtain information on various types of service scenarios;

[0041] Obtain a pre-built decision network;

[0042] Obtain matching information corresponding to the service scenario information and the decision network;

[0043] Based on the matching information, a one-to-one correspondence is established between each of the service scenario information and each of the decision networks;

[0044] Based on the aforementioned relationships, all decision networks are stored in a pre-defined second database to obtain the decision network template library.

[0045] To address the aforementioned technical problems, this application also provides a service provider's distribution device, which employs the following technical solution:

[0046] The first judgment module is used to determine whether a service allocation request input by the user has been received; wherein, the service allocation request carries target service scenario information;

[0047] The first parsing module is used to parse the target service scenario information from the service allocation request if the condition is met.

[0048] The first acquisition module is used to acquire a target decision network corresponding to the target service scenario information from a preset decision network template library; wherein the target decision network is constructed and generated based on preset condition factors and preset service providers;

[0049] The receiving module is used to receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider;

[0050] The processing module is used to solve the policy of the target decision network based on the input parameter values ​​to obtain the optimal solution of the target policy state node corresponding to the policy expectation value.

[0051] The first determining module is used to determine the target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameters, and to allocate the target service provider to the target service scenario corresponding to the target service scenario information.

[0052] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0053] Determine whether a service allocation request input by the user has been received; wherein the service allocation request carries target service scenario information;

[0054] If so, the target service scenario information is parsed from the service allocation request;

[0055] Obtain the target decision network corresponding to the target service scenario information from the preset decision network template library; wherein, the target decision network is generated based on preset condition factors and preset service providers;

[0056] Receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider;

[0057] Based on the input parameter values, the target decision network is used to solve the policy, and the optimal solution of the target policy state node corresponding to the policy expectation value is obtained.

[0058] Based on the optimal solution and the service provider parameters, the target service provider corresponding to the service allocation request is determined, and the target service provider is allocated to the target service scenario corresponding to the target service scenario information.

[0059] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0060] Determine whether a service allocation request input by the user has been received; wherein the service allocation request carries target service scenario information;

[0061] If so, the target service scenario information is parsed from the service allocation request;

[0062] Obtain the target decision network corresponding to the target service scenario information from the preset decision network template library; wherein, the target decision network is generated based on preset condition factors and preset service providers;

[0063] Receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider;

[0064] Based on the input parameter values, the target decision network is used to solve the policy, and the optimal solution of the target policy state node corresponding to the policy expectation value is obtained.

[0065] Based on the optimal solution and the service provider parameters, the target service provider corresponding to the service allocation request is determined, and the target service provider is allocated to the target service scenario corresponding to the target service scenario information.

[0066] Compared with the prior art, the embodiments of this application have the following main advantages:

[0067] This application embodiment, upon receiving a service allocation request from a user, first parses the target service scenario information from the request. Then, it retrieves the target decision network corresponding to the target service scenario information from a pre-set decision network template library. Next, it receives the user-inputted policy expectation value, input parameters corresponding to condition factors, and service provider parameters. Based on the input parameters, it solves the policy in the target decision network to obtain the optimal solution for the target policy state node corresponding to the policy expectation value. Subsequently, based on the optimal solution and the service provider parameters, it determines the target service provider corresponding to the service allocation request and allocates the target service provider to the target service scenario corresponding to the target service scenario information. This application, by calling the target decision network corresponding to the target service scenario information, allows users to quickly and accurately determine the corresponding target service provider and allocate it by simply inputting the corresponding policy expectation value, input parameters, and service provider parameters. This effectively improves the response speed of service providers and enhances the processing efficiency and accuracy of service provider allocation. Attached Figure Description

[0068] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0070] Figure 2 A flowchart of an embodiment of the service provider allocation method according to this application;

[0071] Figure 3 This is a schematic diagram of the structure of one embodiment of the distribution device of the service provider according to this application;

[0072] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0073] Unless otherwise defined, 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 pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0074] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0075] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0076] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0077] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0078] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0079] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0080] It should be noted that the service provider allocation method provided in this application embodiment is generally executed by the server / terminal device, and correspondingly, the service provider allocation device is generally set in the server / terminal device.

[0081] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0082] Continue to refer to Figure 2 A flowchart illustrating an embodiment of a service provider allocation method according to this application is shown. The service provider allocation method includes the following steps:

[0083] Step S201: Determine whether a service allocation request input by the user has been received; wherein the service allocation request carries target service scenario information.

[0084] In this embodiment, the service provider's allocation method operates on electronic devices (e.g., Figure 1 The server / terminal device shown can obtain user-input service allocation requests via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods. The aforementioned service allocation request is user-triggered request data for the electronic device to request the allocation of a corresponding service provider for a rescue service based on target service scenario information. When dispatching rescue services, it is necessary to determine the most suitable service provider from multiple service providers. Furthermore, the aforementioned target service scenario information may be the name information of the service scenario for which a service provider needs to be allocated.

[0085] Step S202: If yes, parse the target service scenario information from the service allocation request.

[0086] In this embodiment, by parsing the service allocation request, the target service scenario information carried in the service allocation request can be obtained.

[0087] Step S203: Obtain the target decision network corresponding to the target service scenario information from the preset decision network template library; wherein, the target decision network is generated based on preset condition factors and preset service providers.

[0088] In this embodiment, a decision network template library can be constructed based on the correlation between various types of service scenario information and the decision network. The specific implementation process of constructing and generating the target decision network based on preset condition factors and preset service providers will be described in further detail in subsequent embodiments and will not be elaborated upon here.

[0089] Step S204: Receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider.

[0090] In this embodiment, the user can input the expected value of the strategy, the input parameter values ​​corresponding to the condition factors, and the service provider parameters corresponding to the service provider, according to actual business usage needs. The expected value of the strategy corresponds to the state value of the strategy state node in the target decision network, the input parameter values ​​are the actual attribute values ​​of the condition factors, and the service provider parameters are the weight parameters of the service provider.

[0091] Step S205: Solve the policy for the target decision network based on the input parameter values ​​to obtain the optimal solution for the target policy state node corresponding to the policy expectation value.

[0092] In this embodiment, by substituting the intermediate state nodes corresponding to the condition factors in the target decision network with the corresponding input parameter values, and then running the target decision network, the optimal solution of the target policy state node that meets the above-mentioned policy expectation value is obtained.

[0093] Step S206: Based on the optimal solution and the service provider parameters, determine the target service provider corresponding to the service allocation request, and allocate the target service provider to the target service scenario corresponding to the target service scenario information.

[0094] In this embodiment, the specific implementation process of determining the target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameters will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0095] Upon receiving a service allocation request from a user, this application first parses the target service scenario information from the request. Then, it retrieves the target decision network corresponding to the target service scenario information from a pre-defined decision network template library. Next, it receives the user's input policy expectation value, input parameters corresponding to condition factors, and service provider parameters. Based on the input parameters, it solves the policy in the target decision network to obtain the optimal solution for the target policy state node corresponding to the policy expectation value. Subsequently, based on the optimal solution and the service provider parameters, it determines the target service provider corresponding to the service allocation request and allocates the target service provider to the target service scenario corresponding to the target service scenario information. This application, by calling the target decision network corresponding to the target service scenario information, allows users to quickly and accurately determine the corresponding target service provider and allocate it by simply inputting the policy expectation value, input parameters, and service provider parameters. This effectively improves the response speed of service providers and enhances the processing efficiency and accuracy of service provider allocation.

[0096] In some alternative implementations, prior to step S203, the electronic device may also perform the following steps:

[0097] Obtain the condition factors corresponding to the target service scenario information from the preset condition factor library.

[0098] In this embodiment, the aforementioned condition factor library can be a pre-built database by relevant users based on their actual business needs, storing various types of service scenario information and condition factors corresponding to each service scenario. Relevant users can dynamically configure data within the condition factor library according to their own needs, exhibiting strong scalability.

[0099] Retrieve the service provider corresponding to the target service scenario information from the preset service provider database.

[0100] In this embodiment, the aforementioned service provider database can be a pre-built database containing various service provider databases, constructed by relevant users according to their actual business needs. Relevant users can dynamically configure data within this service provider database according to their own requirements, exhibiting strong scalability. Here, the service provider corresponding to the target service scenario information refers to a supplier that can provide services for the service scenario corresponding to the target service scenario information.

[0101] The conditional factors are used as decision network strategy factors, and decision tree templates are constructed using the decision network strategy factors.

[0102] In this embodiment, the specific implementation process of using the conditional factors as decision network strategy factors and constructing a decision tree template using the decision network strategy factors will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0103] The target decision network is obtained by using the service provider as the policy state node of the decision tree template.

[0104] In this embodiment, after obtaining the decision tree template, the aforementioned service provider can be further used as the policy state node of the decision tree template to adjust the topology of the decision tree template, thereby obtaining the final target decision network. The aforementioned policy state node can also be referred to as the target branch decision node.

[0105] The target decision network is stored in the decision network template library.

[0106] This application, through a pre-defined condition factor library and service provider database, can quickly obtain the condition factors corresponding to the target service scenario information, as well as the service providers corresponding to the target service scenario information. Then, based on the obtained condition factors and service providers, the required target decision network can be quickly constructed. This is beneficial for quickly and accurately determining the target service provider corresponding to the service allocation request based on the target decision network, thereby effectively improving the response speed of service provider allocation.

[0107] In some optional implementations of this embodiment, the conditional factors are used as decision network policy factors, and the decision tree template is constructed using the decision network policy factors, including the following steps:

[0108] The decision network strategy factors are freely combined to obtain multiple factor combinations.

[0109] In this embodiment, the number of decision network strategy factors mentioned above includes multiple factors. Free combination means combining one or more decision network strategy factors. Each factor combination may contain one decision network strategy factor or include multiple decision network strategy factors.

[0110] The corresponding intermediate state nodes are generated based on the combination of factors.

[0111] In this embodiment, after freely combining all decision network policy factors to obtain multiple factor combinations, intermediate state nodes satisfying these factor combinations can be constructed. Specifically, the decision tree template can be obtained by calculating the intermediate states of the factor combinations and using them as intermediate state nodes in the initial decision tree template. Alternatively, an existing decision tree model can be used as the aforementioned initial decision tree template. For example, if the number of decision network policy factors (hereinafter referred to as factors) is 2, including factor A and factor B, freely combining factor A and factor B yields three factor combinations: a first factor combination containing factor A, a second factor combination containing factor B, and a third factor combination containing both factor A and factor B. Continuing with the example above, by calculating the intermediate states of each factor combination, we can obtain the corresponding 8 intermediate state nodes: intermediate state node 1 (A:TURE, B:NULL), intermediate state node 2 (A:FALSE, B:NULL), intermediate state node 3 (A:NULL, B:TURE), intermediate state node 4 (A:NULL, B:FALSE), intermediate state node 5 (A:TURE, B:TURE), intermediate state node 6 (A:TURE, B:FALSE), intermediate state node 7 (A:FALSE, B:TURE), and intermediate state node 8 (A:FALSE, B:FALSE). Here, TURE indicates that the factor is satisfied, FALSE indicates that the factor is not satisfied, and NULL indicates that no factor is included.

[0112] The decision tree template is constructed based on the intermediate state nodes.

[0113] In this embodiment, the intermediate state nodes are used as the topology of the initial decision tree template to obtain the corresponding decision tree template.

[0114] This application obtains multiple factor combinations by freely combining decision network strategy factors, and then constructs intermediate state nodes corresponding to the factor combinations. Based on the intermediate state nodes, a decision tree template can be quickly generated. This is beneficial for using the decision tree template to generate the corresponding target decision network. Based on the target decision network, the target service provider corresponding to the service allocation request can be quickly and accurately determined, thereby effectively improving the response speed of service provider allocation.

[0115] In some alternative implementations, before the step of obtaining the condition factors corresponding to the target service scenario information from a preset condition factor library, the electronic device may also perform the following steps:

[0116] Determine whether a factor configuration request input from the target user has been received; wherein the factor configuration request carries factor data and the target user's user information.

[0117] In this embodiment, the target user can be a configuration personnel related to the construction of the condition factor library. The factor user refers to the data used to construct the condition factor library. The user information is identification information used to indicate the target user's workplace identity, and this user information may include the target user's name and job title, etc.

[0118] If a factor configuration request is received from the target user, the user information is parsed from the factor configuration request.

[0119] In this embodiment, by parsing the factor configuration request, the corresponding user information can be obtained.

[0120] The target user's permissions are verified based on the user information.

[0121] In this embodiment, the specific implementation process of verifying the permissions of the target user based on the user information will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0122] If the permission verification is successful, the factor data carried in the factor configuration request is obtained.

[0123] In this embodiment, the corresponding factor data can be obtained by parsing the factor configuration request.

[0124] The factor data is stored in a preset first database to obtain the condition factor library.

[0125] In this embodiment, the aforementioned first database can be a pre-built database that does not currently store data. Furthermore, by moving the traditionally complex conditions and hard-coded calculation logic to policy factors, i.e., factor data, and storing them in a condition factor library, code readability, cleanliness, and ease of maintenance can be improved.

[0126] When this application receives a factor configuration request from a target user, it first verifies the user's permissions. After successful verification, it constructs a corresponding conditional factor library based on the factor data input by the target user. This allows the target user to dynamically configure factor data according to actual usage needs, offering strong scalability and improving the user experience. Subsequently, this conditional factor library can be used to quickly and conveniently obtain conditional factors corresponding to the target service scenario information, which helps improve the generation rate of the target decision network. Furthermore, the factor configuration request is only processed after the user has passed permission verification, ensuring the standardization of the factor configuration process and avoiding adverse consequences caused by responding to factor configuration requests from unauthorized users.

[0127] In some optional implementations, the above-mentioned permission verification of the target user based on the user information includes the following steps:

[0128] Invoke the preset permission verification model.

[0129] In this embodiment, the aforementioned permission verification model can specifically be a pre-trained classification tree model. Each node in the classification tree model, except for the leaf nodes, corresponds to a classification rule, and each classification rule categorizes one type of data within the user information. Thus, the classification tree model can classify user information layer by layer, ultimately assigning each user information to a leaf node. Then, based on the preset correspondence between leaf nodes and permission scores, the target permission score corresponding to the user information can be determined.

[0130] The permission verification model determines the user role category corresponding to the user information, and based on the preset correspondence between role category and permission score, the target permission score corresponding to the user role category is determined.

[0131] In this embodiment, for example, if the user information includes: "Job level: 6, Work team: B, Development task: 08", and if the root node of the classification tree model is classified by "Job level", the second-level node is classified by "Work team", and the third-level node is classified by "Development task", then through the three-level classification, the user information can be assigned to a leaf node. Then, according to the preset correspondence between the leaf node and the work team, the target permission score corresponding to the user information can be determined.

[0132] Retrieve the processing permission score corresponding to the business operation configured by the factor from the preset business operation permission table.

[0133] In this embodiment, a business operation permission table is pre-created, which stores the processing permission score corresponding to each business operation.

[0134] Determine whether the target permission score is greater than the processing permission score.

[0135] If the score is greater than the processing permission score, the permission verification is considered successful; otherwise, the permission verification is considered unsuccessful.

[0136] After obtaining user information from the factor configuration request, this application uses a classification tree model to quickly retrieve the target permission score corresponding to the target user's information. This target permission score is then compared with the processing permission score corresponding to the factor configuration's business operation. Based on this comparison, it can accurately and quickly determine whether the target user has the necessary factor configuration permissions. Only after determining the user's factor configuration permissions will the subsequent factor configuration request be processed, ensuring the standardization of the factor configuration process and avoiding adverse consequences from responding to invalid user input factor configuration requests.

[0137] In some optional implementations of this embodiment, step S206 includes the following steps:

[0138] Obtain the number of optimal solutions.

[0139] In this embodiment, the number of the above-mentioned optimal solutions may include one or more.

[0140] If the number of optimal solutions is 1, obtain the first service provider corresponding to the optimal solution, and use the first service provider as the target service provider.

[0141] In this embodiment, if the number of optimal solutions is 1, the service provider corresponding to the optimal solution will be directly selected as the target service provider to be assigned.

[0142] If there are multiple optimal solutions, the weight parameters of the second service provider corresponding to each optimal solution are determined based on the service provider parameters.

[0143] In this embodiment, the aforementioned service provider parameters can be weight parameters of the service providers passed in by the user according to actual usage needs. If there are multiple optimal solutions, the weight parameters of the second service providers corresponding to each optimal solution will be obtained. Subsequently, the weight parameters of each second service provider will be processed according to a random weighting algorithm to obtain the final target service provider.

[0144] Based on the weight parameters, a third service provider is determined from all the second service providers using a preset random weight algorithm.

[0145] In this embodiment, the process of determining a third service provider from all second service providers using a preset random weighting algorithm based on each of the weight parameters may include: calculating the weight ratio of each second service provider based on each of the weight parameters; calculating the coverage area of ​​each second service provider based on the weight ratio; generating a random number using a random number function; determining a target coverage area matching the random number from all the coverage areas; and selecting the service provider corresponding to the target coverage area from all the second service providers as the third service provider. Wherein, the weight ratio of any second service provider = the weight parameter of that second service provider / the total weight, and the total weight is the sum of the weight parameters of all 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, then based on the above data, the weight ratio of service provider 1 can be calculated as 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. Based on the obtained weight ratios, the coverage area of ​​service provider 1 is calculated to be (0,3], the coverage area of ​​service provider 2 is (3,9], and the coverage area of ​​service provider 3 is (9,10]. A random number function is then used to generate a random number between (0,10]. The selection is based on which coverage area the random number falls into. If the random number is 6, falling within the coverage area of ​​(3,9], then service provider 2 is selected as the third service provider.

[0146] The third service provider is designated as the target service provider.

[0147] After obtaining the optimal solution of the target policy state node corresponding to the policy expectation value, this application will use different calculation methods to determine the final target service provider based on the number of optimal solutions, thereby improving the intelligence of the target service provider determination and ensuring the accuracy of the obtained target service provider.

[0148] In some optional implementations of this embodiment, before step S203, the electronic device may further perform the following steps:

[0149] Obtain information on various types of service scenarios.

[0150] In this embodiment, service scenarios are pre-classified into various types according to actual business needs, and service scenario information can refer to the name information of various service scenarios.

[0151] Obtain a pre-built decision network.

[0152] In this embodiment, for each different type of service scenario, a corresponding decision network will be constructed in advance based on the service scenario, and the construction method of the decision network corresponding to each service scenario can refer to the construction method of the target decision network mentioned above, which will not be elaborated in detail here.

[0153] Obtain matching information corresponding to the service scenario information and the decision network.

[0154] In this embodiment, the matching information mentioned above may be information pre-input by the relevant user that identifies the matching relationship between service scenario information and the decision network.

[0155] Based on the matching information, a one-to-one correspondence is established between each of the service scenario information and each of the decision networks.

[0156] Based on the aforementioned relationships, all decision networks are stored in a pre-defined second database to obtain the decision network template library.

[0157] In this embodiment, the second database can be a pre-built database that does not currently store data. Furthermore, after obtaining the decision network template library, it can be cached in memory.

[0158] This application constructs a decision network template library based on the correlation between various types of service scenario information and decision networks. This enables the system to quickly and conveniently retrieve the target decision network corresponding to the target service scenario information input by the user from the decision network template library. Furthermore, based on the target decision network, the system can quickly and accurately generate the target service provider corresponding to the service allocation request, effectively improving the response speed of service provider allocation.

[0159] It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned strategy expectation value, the above-mentioned strategy expectation value can also be stored in a node of a blockchain.

[0160] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0161] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0162] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0164] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0165] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a service provider's distribution device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0166] like Figure 3As shown, the service provider allocation device 300 described in this embodiment includes: a first judgment module 301, a first parsing module 302, a first acquisition module 303, a receiving module 304, a processing module 305, and a first determination module 306. Wherein:

[0167] The first judgment module 301 is used to determine whether a service allocation request input by the user has been received; wherein, the service allocation request carries target service scenario information;

[0168] The first parsing module 302 is used to parse the target service scenario information from the service allocation request if the condition is met.

[0169] The first acquisition module 303 is used to acquire a target decision network corresponding to the target service scenario information from a preset decision network template library; wherein the target decision network is constructed and generated based on preset condition factors and preset service providers;

[0170] The receiving module 304 is used to receive the policy expectation value input by the user, the input parameter value corresponding to the condition factor, and the service provider parameter corresponding to the service provider;

[0171] The processing module 305 is used to solve the strategy of the target decision network based on the input parameter value, and obtain the optimal solution of the target strategy state node corresponding to the expected value of the strategy;

[0172] The first determining module 306 is used to determine the target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameters, and to allocate the target service provider to the target service scenario corresponding to the target service scenario information.

[0173] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0174] In some optional implementations of this embodiment, the service provider's allocation device further includes:

[0175] The second acquisition module is used to acquire condition factors corresponding to the target service scenario information from a preset condition factor library;

[0176] The third acquisition module is used to acquire service providers corresponding to the target service scenario information from a preset service provider database.

[0177] The construction module is used to use the conditional factors as decision network strategy factors and to construct a decision tree template using the decision network strategy factors.

[0178] The second determining module is used to use the service provider as a strategy state node of the decision tree template to obtain the target decision network;

[0179] The first storage module is used to store the target decision network in the decision network template library.

[0180] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0181] In some optional implementations of this embodiment, the building module includes:

[0182] The combination submodule is used to freely combine the decision network strategy factors to obtain multiple factor combinations;

[0183] A generation submodule is used to generate corresponding intermediate state nodes based on the factor combination;

[0184] A submodule is constructed to build the decision tree template based on the intermediate state nodes.

[0185] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0186] In some optional implementations of this embodiment, the service provider's allocation device further includes:

[0187] The second judgment module is used to determine whether a factor configuration request input by the target user has been received; wherein the factor configuration request carries factor data and the user information of the target user;

[0188] The second parsing module is used to parse the user information from the factor configuration request if a factor configuration request is received from the target user.

[0189] The verification module is used to verify the permissions of the target user based on the user information;

[0190] The fourth acquisition module is used to acquire the factor data carried in the factor configuration request if the permission verification is successful.

[0191] The second storage module is used to store the factor data in a preset first database to obtain the condition factor library.

[0192] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0193] In some optional implementations of this embodiment, the verification module includes:

[0194] Call the submodule to invoke the preset permission verification model;

[0195] The first determining submodule is used to determine the user role category corresponding to the user information through the permission verification model, and to determine the target permission score corresponding to the user role category based on the preset correspondence between role category and permission score;

[0196] The query submodule is used to retrieve the processing permission score corresponding to the business operation configured by the factor from the preset business operation permission table.

[0197] The judgment submodule is used to determine whether the target permission score is greater than the processing permission score;

[0198] The first determination submodule is used to determine that the permission verification is passed if the score is greater than the processing permission score, otherwise the permission verification is failed.

[0199] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0200] In some optional implementations of this embodiment, the first determining module 306 includes:

[0201] A submodule for obtaining the number of optimal solutions;

[0202] The second determination submodule is used to obtain the first service provider corresponding to the optimal solution if the number of optimal solutions is 1, and to use the first service provider as the target service provider.

[0203] The second determining submodule is used to determine the weight parameters of the second service provider corresponding to each of the optimal solutions based on the service provider parameters if there are multiple optimal solutions.

[0204] The third determining submodule is used to determine the third service provider from all the second service providers based on each of the weight parameters using a preset random weight algorithm;

[0205] The third determination submodule is used to identify the third service provider as the target service provider.

[0206] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0207] In some optional implementations of this embodiment, the service provider's allocation device further includes:

[0208] The fifth acquisition module is used to acquire information about various types of service scenarios;

[0209] The sixth acquisition module is used to acquire the pre-built decision network;

[0210] The seventh acquisition module is used to acquire matching information corresponding to the service scenario information and the decision network;

[0211] The association module is used to establish a one-to-one association relationship between each of the service scenario information and each of the decision networks based on the matching information;

[0212] The third storage module is used to store all the decision networks in a preset second database based on the association relationship, thereby obtaining the decision network template library.

[0213] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the service provider allocation method in the aforementioned implementation method, and will not be repeated here.

[0214] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0215] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0216] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0217] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), 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 disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or 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, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for service provider allocation methods, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0218] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, such as executing computer-readable instructions of the service provider's allocation method.

[0219] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0220] Compared with the prior art, the embodiments of this application have the following main advantages:

[0221] In this embodiment, after receiving a service allocation request from a user, the system first parses the target service scenario information from the request. Then, it retrieves the target decision network corresponding to the target service scenario information from a pre-defined decision network template library. Next, it receives the user's input policy expectation value, input parameters corresponding to condition factors, and service provider parameters. Based on the input parameters, it solves the policy in the target decision network to obtain the optimal solution for the target policy state node corresponding to the policy expectation value. Subsequently, based on the optimal solution and the service provider parameters, it determines the target service provider corresponding to the service allocation request and allocates the target service provider to the target service scenario corresponding to the target service scenario information. This application, by calling the target decision network corresponding to the target service scenario information, allows users to quickly and accurately determine the corresponding target service provider and allocate it by simply inputting the corresponding policy expectation value, input parameters, and service provider parameters. This effectively improves the response speed of service providers and enhances the processing efficiency and accuracy of service provider allocation.

[0222] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the service provider allocation method described above.

[0223] Compared with the prior art, the embodiments of this application have the following main advantages:

[0224] In this embodiment, after receiving a service allocation request from a user, the system first parses the target service scenario information from the request. Then, it retrieves the target decision network corresponding to the target service scenario information from a pre-defined decision network template library. Next, it receives the user's input policy expectation value, input parameters corresponding to condition factors, and service provider parameters. Based on the input parameters, it solves the policy in the target decision network to obtain the optimal solution for the target policy state node corresponding to the policy expectation value. Subsequently, based on the optimal solution and the service provider parameters, it determines the target service provider corresponding to the service allocation request and allocates the target service provider to the target service scenario corresponding to the target service scenario information. This application, by calling the target decision network corresponding to the target service scenario information, allows users to quickly and accurately determine the corresponding target service provider and allocate it by simply inputting the corresponding policy expectation value, input parameters, and service provider parameters. This effectively improves the response speed of service providers and enhances the processing efficiency and accuracy of service provider allocation.

[0225] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0226] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method of allocating service providers, characterized by, The method comprises the following steps: determining whether a service allocation request input by a user is received; wherein the service allocation request carries target service scenario information; if so, parsing the target service scenario information from the service allocation request; obtaining a target decision network corresponding to the target service scenario information from a preset decision network template library; wherein the target decision network is generated based on a preset condition factor and a preset service provider; receiving a policy expectation value input by the user, a parameter value corresponding to the condition factor, and a service provider parameter corresponding to the service provider; solving a policy of the target decision network based on the parameter value to obtain an optimal solution of a target policy state node corresponding to the policy expectation value; 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 scenario corresponding to the target service scenario information; wherein the step of determining the target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter comprises: obtaining the number of optimal solutions; if the number of optimal solutions is one, obtaining a first service provider corresponding to the optimal solution and taking the first service provider as the target service provider; if the number of optimal solutions is multiple, determining a weight parameter of a second service provider corresponding to each of the optimal solutions based on the service provider parameter; determining a third service provider from all the second service providers based on the weight parameters using a preset random weight algorithm; taking the third service provider as the target service provider; wherein the process of determining the third service provider from all the second service providers based on the weight parameters using the preset random weight algorithm comprises: calculating a weight proportion of each of the second service providers based on the weight parameters; calculating a coverage area of each of the second service providers based on the weight proportion; generating a random number using a random number function; determining a target coverage area matching the random number from all the coverage areas; and selecting a service provider corresponding to the target coverage area from all the second service providers as the third service provider; wherein the weight proportion of any second service provider = the weight parameter of the second service provider / total weight, and the total weight is the sum of the weight parameters of all the second service providers.

2. The method of claim 1, wherein, Before the step of obtaining the target decision network corresponding to the target service scenario information from the preset decision network template library, the method further comprises: obtaining a condition factor corresponding to the target service scenario information from a preset condition factor library; obtaining a service provider corresponding to the target service scenario information from a preset service provider database; using the condition factor as a decision network policy factor and constructing a decision tree template using the decision network policy factor; using the service provider as a policy state node of the decision tree template to obtain the target decision network; storing the target decision network in the decision network template library.

3. The method of claim 2, wherein, The step of taking the condition factor as a decision network strategy factor and constructing a decision tree template using the decision network strategy factor specifically includes: freely combining the decision network strategy factors to obtain multiple factor combinations; generating corresponding intermediate state nodes based on the factor combinations; constructing the decision tree template based on the intermediate state nodes.

4. The method of claim 2, wherein the service provider is allocated based on a service provider's ability to provide the service. Before the step of obtaining the condition factor corresponding to the target service scenario information from the preset condition factor library, the method further includes: determining 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 the factor configuration request input by the target user is received, parsing the user information from the factor configuration request; performing permission verification on the target user based on the user information; if the permission verification is passed, obtaining the factor data carried in the factor configuration request; storing the factor data in a preset first database to obtain the condition factor library.

5. The method of claim 4, wherein, The step of performing permission verification on the target user based on the user information specifically includes: calling a preset permission verification model; determining a user role category corresponding to the user information through the permission verification model, and determining a target permission score corresponding to the user role category based on a preset correspondence between role categories and permission scores; querying a processing permission score corresponding to a business operation of factor configuration from a preset business operation permission table; determining whether the target permission score is greater than the processing permission score; if the target permission score is greater than the processing permission score, it is determined that the permission verification is passed, otherwise it is determined that the permission verification is not passed.

6. The method of claim 1, wherein the service provider is selected based on a service provider's ability to provide the service. Before the step of obtaining the target decision network corresponding to the target service scenario information from the preset decision network template library, the method further includes: obtaining various types of service scenario information; obtaining a pre-constructed decision network; obtaining matching information corresponding to the service scenario information and the decision network; based on the matching information, establishing a one-to-one association between each service scenario information and each decision network; based on the association, storing all the decision networks in a preset second database to obtain the decision network template library.

7. A service provider distribution apparatus, characterized by comprising: includes: a first determination module configured to determine whether a service allocation request input by a user is received; wherein the service allocation request carries target service scenario information; a first parsing module configured to parse the target service scenario information from the service allocation request if the service allocation request is received; a first obtaining module configured to obtain a target decision network corresponding to the target service scenario information from a preset decision network template library; wherein the target decision network is constructed and generated based on a preset condition factor and a preset service provider; a receiving module configured to receive a policy expectation value input by the user, an input parameter value corresponding to the condition factor, and a service provider parameter corresponding to the service provider; a processing module configured to perform policy solving on the target decision network based on the input parameter value to obtain an optimal solution of a target policy state node corresponding to the policy expectation value; The first determining module is configured to determine a target service provider corresponding to the service allocation request based on the optimal solution and the service provider parameter, and allocate the target service provider to a target service scene corresponding to the target service scene information. The first determining module includes: The obtaining sub-module is configured to obtain the number of the optimal solutions. The second determining sub-module is configured to, if the number of the optimal solutions is one, obtain a first service provider corresponding to the optimal solution, and take the first service provider as the target service provider. The second determining sub-module is configured to, if the number of the optimal solutions is a plurality, determine a weight parameter of a second service provider corresponding to each of the optimal solutions based on the service provider parameter. The third determining sub-module is configured to determine a third service provider from all the second service providers based on the weight parameters and using a preset random weight algorithm. The third determining sub-module is configured to take the third service provider as the target service provider. The process of determining the third service provider from all the second service providers based on the weight parameters and using the preset random weight algorithm includes: calculating a weight proportion of each of the second service providers based on the weight parameters; calculating a coverage area of each of the second service providers based on the weight proportion; generating a random number using a random number function; determining a target coverage area matching the random number from all the coverage areas; and screening a service provider corresponding to the target coverage area from all the second service providers as the third service provider. The weight proportion of any one of the second service providers is equal to the weight parameter of the second service provider / the total weight, and the total weight is the sum of the weight parameters of all the second service providers.

8. A computer device including a memory and a processor, the memory storing computer readable instructions, and the processor implementing the steps of the service provider allocation method according to any one of claims 1 to 6 when executing the computer readable instructions.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to implement the steps of the service provider allocation method according to any one of claims 1 to 6.

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