Service resource determination method, apparatus and system

Abstract

This disclosure provides a service resource determination method, apparatus, and system, relating to the field of information processing technology. The method includes: acquiring service resource information to be analyzed and first service resource information, the service resource information to be analyzed including service resource node information; constructing a graph database based on the first service resource information; and determining the corresponding target service resource node information using a knowledge representation model based on the service resource node information and the graph database when the service resource node information belongs to the first service resource information. This disclosure can solve the problems of high manpower and material resources consumption, low work efficiency, and impact on product quality in existing technologies.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of information processing, and in particular, to a service resource determination method and device, and a service resource determination system. BACKGROUND

[0002] Service resources are important components of a telecommunications network operation system and are the basis for forming service products. Various types of service resources have complex interrelationships, and in the creation process of service products, information filtering and optimization of each service resource and its interrelationships are required.

[0003] In related technologies, relevant business experts manually filter various types of data files to prepare for the construction of service products. However, this manual filtering method requires a large amount of manpower and resources and has very low efficiency, which is not conducive to rapid product online. In addition, when facing large-scale data, it is difficult to obtain ideal results through manual filtering, which affects product quality.

[0004] It should be noted that the information disclosed in the foregoing background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the embodiments of the present disclosure is to provide a service resource determination method and a service scheduling method and system, thereby to some extent solving the problems of related technologies, such as the consumption of manpower and resources, low efficiency, and the impact on product quality.

[0006] According to a first aspect of the present disclosure, a service resource determination method is provided, the method comprising:

[0007] obtaining to-be-analyzed service resource information and first service resource information, the to-be-analyzed service resource information comprising service resource node information; based on the first service resource information, constructing a graph database; when the service resource node information belongs to the first service resource information, based on the service resource node information and the graph database, determining corresponding target service resource node information using a knowledge representation model.

[0008] In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the constructing a graph database comprises: obtaining second service resource information; based on the first service resource information and the second service resource information, constructing a service resource object node file and a file of interrelationships between nodes; based on the service resource object node file and the file of interrelationships, forming structured data, and storing the structured data in the graph database.

[0009] In an example embodiment of the present disclosure, based on the foregoing scheme, the method further comprises: performing word segmentation processing on each type of business text to obtain the first service resource information.

[0010] In an example embodiment of the present disclosure, based on the foregoing scheme, the training process of the knowledge representation model comprises: extracting corresponding triple information based on the second service resource information to form training data; the triple information comprises a head node, a tail node, and an association relationship between the head node and the tail node; the head node, the tail node, and the association relationship in the training data are respectively subjected to vectorization processing; based on the head node corresponding vector, the tail node corresponding vector, and the association relationship corresponding vector, a loss function value is calculated; based on the loss function value, the parameters of the head node corresponding vector, the parameters of the tail node corresponding vector, and the parameters of the association relationship corresponding vector are updated.

[0011] In an example embodiment of the present disclosure, based on the foregoing scheme, the to-be-analyzed service resource information comprises two of the to-be-analyzed triple node information; and the determination of the corresponding target service resource node information by using the knowledge representation model comprises: performing vectorization representation on the two node information in the to-be-analyzed triple node information respectively; based on the vectorization representation of the two node information, a target vector of the remaining one node information in the to-be-analyzed triple information is calculated by using a target function to obtain a target service resource vector; and based on the target service resource vector, the target service resource node information is determined.

[0012] In an example embodiment of the present disclosure, based on the foregoing scheme, the method further comprises: sending the target service resource information to the terminal device to enable the terminal device to display the target service resource information.

[0013] According to a second aspect of the present disclosure, a service resource determination system is provided, comprising: a user terminal and a server end, the user terminal is configured to input to-be-analyzed service resource information; the server end is configured to obtain the to-be-analyzed service resource information and first service resource information, the to-be-analyzed service resource information comprises service resource node information; based on the first service resource information, a graph database is constructed; when the service resource node information belongs to the first service resource information, based on the service resource node information and the graph database, a knowledge representation model is used to determine corresponding target service resource node information.

[0014] According to a third aspect of the present disclosure, a service resource determination apparatus comprises a first acquisition module, a construction module, and a prediction module. The first acquisition module is configured to acquire to-be-analyzed service resource information and first service resource information, wherein the to-be-analyzed service resource information comprises service resource node information. The construction module is configured to construct a graph database based on the first service resource information. The prediction module is configured to determine corresponding target service resource node information based on the service resource node information and the graph database when the service resource node information belongs to the first service resource information.

[0015] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the method according to any one of the preceding aspects.

[0016] According to a fifth aspect of the present disclosure, a network device is provided, which comprises a processor and a memory storing executable instructions of the processor. The processor is configured to execute the method according to any one of the preceding aspects by executing the executable instructions.

[0017] The example embodiments of the present disclosure can have the following partial or all beneficial effects:

[0018] In the service resource determination method provided by the example embodiments of the present disclosure, a graph database can be constructed based on first service resource information. When service resource node information belongs to the first service resource information, corresponding target service resource node information can be determined based on the service resource node information and the graph database by using a knowledge representation model. On the one hand, the knowledge representation model can be used to quickly predict the service resource node information belonging to the first service resource information, thereby improving the service resource screening efficiency for service products and saving manpower and resources. On the other hand, the input model of the service resource node information not belonging to the first service resource information can be avoided, thereby reducing the model data processing amount and improving the work efficiency.

[0019] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not intended to limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and serve to explain the principles of the present disclosure. It is apparent that the accompanying drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0021] Figure 1 An exemplary application scenario architecture diagram is shown schematically in accordance with one embodiment of the present disclosure.

[0022] Figure 2 A service resource determination method diagram is shown schematically in accordance with one embodiment of the present disclosure.

[0023] Figure 3 A training process diagram of a knowledge representation model is shown schematically in accordance with one embodiment of the present disclosure.

[0024] Figure 4 A process diagram in which a knowledge representation model determines target service resource information is shown schematically in accordance with one embodiment of the present disclosure.

[0025] Figure 5 A service resource determination method diagram is shown schematically in accordance with one embodiment of the present disclosure.

[0026] Figure 6 A structural block diagram of a service resource determination apparatus is shown schematically in accordance with one embodiment of the present disclosure.

[0027] Figure 7 A structural block diagram of a service resource determination system is shown schematically in accordance with one embodiment of the present disclosure.

[0028] Figure 8 A network device block diagram suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0029] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0030] In addition, the accompanying drawings are included to provide a thorough understanding of the present disclosure and are not intended to be exhaustive or to limit the present disclosure to the precise outline as illustrated. The same or similar components are denoted by the same reference numerals throughout the drawings, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. These functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] Figure 1 A schematic diagram of a system architecture 100 illustrating an exemplary application environment of a service resource determination method and apparatus to which embodiments of the present disclosure can be applied is shown. As shown, the system architecture 100 can include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium to provide a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links, or fiber optic cables, and the like. The terminal devices 101, 102, 103 can be various network devices with display screens, including but not limited to desktop computers, portable computers, smart phones, and tablet computers, and the like. Figure 1

[0032] The server 105 can be a server that provides various services, such as obtaining service resource information to be analyzed and first service resource information from the terminal devices 101, 102, 103; constructing a graph database based on the first service resource information; and when the service resource node information belongs to the first service resource information, determining corresponding target service resource node information based on the service resource node information and the graph database using a knowledge representation model.

[0033] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the server 105 is software, it can be implemented as multiple software or software modules, or as a single software or software module. No specific limitation is made herein.

[0034] It should be understood that the number of terminal devices, networks, and servers in the system architecture 100 is merely illustrative. Any number of terminal devices, networks, and servers can be provided as needed. For example, the server 105 can be a server cluster composed of multiple servers. Figure 1 The service resource determination method provided by the embodiments of the present disclosure can be executed on the server 105, and accordingly, the service resource determination apparatus is generally provided in the server 105.

[0035] ​​

[0036] Reference Figure 2 As shown in the accompanying drawings, the service resource determination method of an example embodiment provided by the present disclosure can be applied to a server. The method can include:

[0037] In step S210, service resource information to be analyzed and first service resource information are obtained.

[0038] In the example embodiment, the service resource information to be analyzed refers to service resource information of a service product. The service resource information to be analyzed can include service resource node information, business category information, etc. The service resource node information can include service resource object information and association relationship information between objects. The service resource object information can include information such as the name, code, and type of the service resource object. The association relationship information between objects can include association object-related information and association relationship-related information. The association object-related information can include information such as the name, code, and type of the association object; and the association relationship-related information can include information such as the name, code, and identifier of the association relationship.

[0039] In the example embodiment, the first service resource information refers to service resource node information that is not in an existing service resource node database. The existing service resource node database can be a node database established by relevant business experts based on experience, or other existing databases, which are not limited in the example. The first service resource information can include first service resource object information (such as object name, code, type, etc.) and / or association relationship information (such as association relationship name, code, identifier, etc.) of the object.

[0040] In step S220, a graph database is constructed based on the first service resource information.

[0041] In the example embodiment, the first service resource information can be input into an existing open source database to form a graph database. For example, the first service resource information can be input into Neo4J to form a graph database. The graph database includes multiple nodes, one node refers to one service resource object, the object can have multiple labels, and each label can be used to represent the type of the object. Exemplarily, the type of the service resource object can be CFS (Customer Facing Service, customer-facing service), RFS (Resource Facing Service, resource-facing service), or RES (Resource, resource). Each object can also have multiple attributes, each attribute is used to describe a corresponding feature, and multiple directional relationships can also be formed between nodes.

[0042] Step S230, when the service resource node information belongs to the first service resource information, determining the corresponding target service resource node information based on the service resource node information and the graph database by using the knowledge representation model.

[0043] In the example embodiment, the graph database can be used to directly locate the service resource node information in the graph network structure, accelerating the screening process of the nodes. In addition, the graph database can be combined with the front-end technology to realize the display of the network topology data.

[0044] In the example embodiment, the knowledge representation model can be used to vectorize the entities and relationships of the knowledge graph. The knowledge representation model can be a translation model of knowledge graph embedding, such as TransE, TransH, TransR, TransD, etc., which is not limited in the example.

[0045] In the service resource determination method provided in the example embodiment, the graph database can be constructed based on the first service resource information. When the service resource node information belongs to the first service resource information, the corresponding target service resource node information is determined based on the service resource node information and the graph database by using the knowledge representation model. On the one hand, the knowledge representation model can be used to quickly predict the service resource node information belonging to the first service resource information, thereby improving the service resource screening efficiency for the service product and saving manpower and resources. At the same time, the product quality risk caused by large-scale data manual screening can be avoided. On the other hand, the service resource node information not belonging to the first service resource information can be avoided to input the model, which can reduce the model data processing amount and improve the work efficiency.

[0046] In another embodiment, the above steps are described in more detail as follows.

[0047] In some embodiments, constructing the graph database comprises:

[0048] Obtaining second service resource information.

[0049] In the example embodiment, the second service resource information refers to the service resource node information in the existing service resource node database. The existing service resource node database can be a node database established by relevant business experts based on experience, or other existing databases, which are not limited in the example. The second service resource information can include second service resource object information (such as object name, code, type, etc.) and / or associated relationship information of the object (such as associated relationship name, code, identifier, etc.).

[0050] Based on the first service resource information and the second service resource information, a service resource object node file and an associated relationship file between nodes are constructed.

[0051] In the example embodiment, the service resource object node file stores the first service resource object information and the second service resource object information, such as the name, code, type, and other information of each object. One object can be taken as one node. The association relationship file between nodes stores the association relationship information between the first service resource objects and the association relationship information between the second service resource objects. The association relationship can be bearing, using, containing, depending, or managing, etc.

[0052] Based on the service resource object node file and the association relationship file, the structured data is formed, and the structured data is stored in the graph database.

[0053] In the example embodiment, the service resource object node and the association relationship corresponding to the node can form a set of structured data. The structured data is stored in the graph database in the form of nodes and connection edges between nodes. In some embodiments, the network topology data of the graph database can also be displayed on the front-end page through the Neo4J database, combined with the front-end Vue and D3.js technologies.

[0054] In an embodiment, the method further comprises: performing word segmentation processing on each type of business text to obtain the first service resource information.

[0055] In the example embodiment, the first service resource information can be obtained from each type of business text, and the first service resource information can contain potential service resource nodes, so as to expand the scope of service resources and expand the use scenarios of the present disclosure.

[0056] In the example embodiment, the word segmentation processing can be performed on each type of business text, and then the keyword extraction is performed on the word segmentation result to obtain the first service resource information. The first service resource information can also be further determined from the word segmentation result or the extracted keywords according to the expert experience, which is not limited in the example. In the example, each type of business text can be the text data recorded by each operator for a certain business. For example, the text data generated in the process of handling the special network cable business for an enterprise, such as the enterprise name, IP address, business type, and other information.

[0057] In an embodiment, the method further comprises: Figure 3 The training process of the knowledge representation model comprises the following steps.

[0058] Based on the second service resource information, the corresponding triple information is extracted to form the training data.

[0059] In this example implementation, the second service resource objects and their associated relationships can be extracted from the second service resource information. These object nodes and their relationships are then integrated into triplet information, including a head node, a tail node, and the relationship between the head and tail nodes. The associated relationship information can be a directed edge between two nodes in a graph database. In this example, a triplet can be used as a training sample.

[0060] The head node, tail node, and relationships in the training data are vectorized respectively.

[0061] In this example implementation, embedding can be used to vectorize the head node, tail node, and association relationship respectively, mapping the node or association relationship into a low-dimensional vector.

[0062] The loss function value is calculated based on the vectors corresponding to the head node, the vectors corresponding to the tail node, and the vectors corresponding to the association relationships.

[0063] In this example implementation, negative sample vectors can be randomly generated for the vectors corresponding to the head node and the vectors corresponding to the tail node. The loss function can be the distance d between the vectors corresponding to the positive samples. p The distance d between the vectors corresponding to the negative samples n The difference. The loss function formula is as follows:

[0064]

[0065] Where γ represents a constant, h p h n Let r represent the vectors corresponding to the head nodes in the positive and negative samples, respectively. p r n Let t represent the vectors corresponding to the association relationships in the positive and negative samples, respectively. p t n Let ||·||2 represent the vectors corresponding to the tail nodes in the positive and negative samples, and let ||·||2 represent the 2-norm.

[0066] The loss function in this example can also be a corresponding form of other knowledge representation models; this example does not limit this.

[0067] Based on the loss function value, update the parameters of the vector corresponding to the head node, the vector corresponding to the tail node, and the vector corresponding to the association relationship.

[0068] In this example implementation, the parameters of the vectors corresponding to each node can be updated using the stochastic gradient descent method, namely the parameters of the vectors corresponding to the head node, the tail node, and the association relationship.

[0069] In some embodiments, reference Figure 4The to-be-analyzed service resource information includes two of the to-be-analyzed triple node information; the to-be-analyzed triple node information can include to-be-analyzed head node 410, to-be-analyzed tail node 420, and to-be-analyzed node association relationship 430. The corresponding target service resource node information is determined by using the knowledge representation model, including:

[0070] The two node information in the to-be-analyzed triple node information is respectively represented by a vector.

[0071] In the example embodiment, the trained knowledge representation model can be used to vectorize the node information. For example, the to-be-analyzed head node 410 and the to-be-analyzed tail node 420 can be vectorized to obtain a second vector, the to-be-analyzed head node 410 and the to-be-analyzed node association relationship 430 can also be vectorized to obtain a first vector, and the to-be-analyzed tail node 420 and the to-be-analyzed node association relationship 430 can also be vectorized to obtain a third vector. The disclosure can predict the other node information in the triple based on two node information.

[0072] Based on the vectorized representation of the two node information, the corresponding vector of the remaining one node information in the to-be-analyzed triple information is calculated by using the target function to obtain the target service resource vector.

[0073] In the example embodiment, any two node vectors in the to-be-analyzed triple can be substituted into the target function to calculate the remaining one node vector, which is the target service resource vector. For example, the tail node vector is predicted by the first vector; the association relationship vector is predicted by the second vector; and the head node vector is predicted by the third vector. The target function can be the sum of the corresponding vector of the to-be-analyzed head node and the corresponding vector of the to-be-analyzed node association relationship equal to the corresponding vector of the to-be-analyzed tail node. The target function can also be the corresponding target function of other knowledge representation models, which is not limited in this example.

[0074] Based on the target service resource vector, the target service resource node information is determined.

[0075] In the example embodiment, the corresponding target service resource node information can be obtained by using the knowledge representation model based on the target service resource vector. For example, the target tail node is obtained by using the tail node vector, the target head node is obtained by using the head node vector, and the target association relationship is obtained by using the association relationship vector. The target service resource information can include multiple service resource node information, and the multiple service resource node information can also be arranged in order of probability size. The number of nodes in the target service resource information in this example can be determined according to user demand, such as setting the number of nodes to 5. The number of nodes can also be determined by the user's corresponding input information on the terminal device.

[0076] In some embodiments, the method further comprises: sending the target service resource information to the terminal device, so that the terminal device displays the target service resource information.

[0077] In the example embodiment, the query request of the user on the front-end page can be responded using the Spring Boot framework. The target service resource information can be displayed by the Neo4J database in combination with front-end technologies such as Vue and D3.js.

[0078] For example, with reference to Figure 5 For an example embodiment of the present disclosure, the service resource determination process of the service product is implemented by the following steps.

[0079] In step S501, the user inputs the service resource information to be analyzed on the terminal device, so that the server obtains the service resource information to be analyzed.

[0080] In the example embodiment, the service resource information to be analyzed can be any two of the triple nodes to be analyzed. For example, it can be the head node to be analyzed and the tail node to be analyzed. The present disclosure aims to establish a knowledge graph between service resource nodes for a new service product, so as to accelerate the development cycle of the telecommunication service product.

[0081] In step S502, the server obtains the first service resource information from the business system.

[0082] In the example embodiment, the first service resource information can be obtained from various business texts. The first service resource information refers to the service resource node information that is not in the existing database, i.e., it can include potential service resource node information.

[0083] In step S503, the server obtains the second service resource information.

[0084] In step S504, the server trains the knowledge representation model based on the second service resource information.

[0085] In the above embodiment, the second service resource information refers to the service resource node information in the existing database. The existing database can be a resource node database formed in the past product development process or a resource node database established according to expert experience. Therefore, the triple node information in the second service resource information is known, and the known triple node information is used as training data to train the knowledge representation model. For example, the open-source OpenKE platform is used to train the knowledge representation model, and the knowledge representation model can be any one of TransE, TransH, TransR, and TransD. In the example, the above four models can also be trained using the training data, and finally the best model is selected as the knowledge representation model of the present disclosure.

[0086] Step S505, the server constructs a graph database based on the first service resource information and the second service resource information.

[0087] In the present example embodiment, the triple node information (the head node, the tail node and the association relationship between the two nodes) can be extracted from the first service resource information and the second service resource information respectively. The head node and the tail node information in the two kinds of resource information are stored in the service resource object node file. The association relationship information is stored in the association relationship file.

[0088] The service resource object node file stores the basic information of the service resource object, which can include object name, object code and object type, etc. For example, the object name is cloud-to-cloud private line CFS, the object code is cfsCloudToCloud, and the object type is CFS. It can also include primary key ID information (such as primary key ID 1).

[0089] The association relationship file stores the association relationship information between service resource objects. The association relationship information can include the primary key ID, name and code of the source service resource object, the primary key ID, name and code of the target service resource object, and the name of the association relationship, which can include bearing, using, containing, depending, managing, etc. For example, the primary key ID of the source service resource object is 2, the name of the source service resource object is fixed access network line CFS, the code of the source service resource object is cfsFixAccessNetLine, the primary key ID of the target service resource object is 3, the name of the target service resource object is user IP segment RES, the code of the target service resource object is resCustomIpSeg, and the name of the association relationship is using. It can also include the primary key ID information of the group of information (such as primary key ID 11).

[0090] Step S506, the server determines whether the service resource information to be analyzed belongs to the first service resource information. If yes, go to step 507, otherwise go to step S508.

[0091] In the present example embodiment, it can be determined whether the node in the service resource information to be analyzed belongs to the potential node in the first service resource information.

[0092] Step S507, the server uses the trained knowledge representation model to predict the association service resource node information of the service resource information to be analyzed, and determines the corresponding target service resource node information.

[0093] Step S508, determine the corresponding target service resource node information through the existing database.

[0094] In the present example, it can be directly determined in the graph database.

[0095] Step S509, the terminal device displays the determined target service resource node information.

[0096] In the present example, the target service resource node information can contain multiple, which can be sorted by probability size and then displayed.

[0097] The present disclosure predicts potential service resource nodes through a knowledge representation model, and the predicted target service resource node information is provided for users (such as product developers) for further review and verification, greatly reducing the data screening range in the product development process, and greatly improving the construction efficiency of the relationship of the service resources of the telecommunications product. The small amount of target service resource node information determined by the present disclosure can be further analyzed and judged by relevant business experts, quickly determine the nodes and relationships related to the product, so as to realize the rapid development and online of the product, greatly reducing the development cycle of the product. The development product of the present disclosure can be a related product for serving the next-generation cloud network operation system.

[0098] The present disclosure aims at the complex relationships between various resource nodes in the telecommunications network system, such as constituent relationship, group-sub relationship, master-slave relationship, mutual exclusion relationship, forced dependency relationship, binding relationship, bidirectional binding relationship, collinear relationship, pay-on-behalf relationship, and dependency relationship. By integrating existing telecommunications network product service resource relationship triple data (second service resource information), training a knowledge representation model, and successfully realizing the screening of potential data node relationships from a large amount of business arrangement data files. The candidate target data is provided for users for further analysis and verification, which greatly reduces the data screening range for users.

[0099] Further, in the present example embodiment, a service resource determination apparatus 600 is also provided, which comprises a first acquisition module 610, a construction module 620, and a determination module 630. The first acquisition module 610 is configured to acquire service resource information to be analyzed and first service resource information, wherein the service resource information to be analyzed comprises service resource node information; the construction module 620 is configured to construct a graph database based on the first service resource information; and the determination module 630 is configured to, when the service resource node information belongs to the first service resource information, determine corresponding target service resource node information based on the service resource node information and the graph database by using a knowledge representation model.

[0100] In an exemplary embodiment of the present disclosure, the construction module 620 comprises a second acquisition module configured to acquire second service resource information; a construction sub-module configured to construct a service resource object node file and an association relationship file between nodes based on the first service resource information and the second service resource information; and a storage module configured to form structured data based on the service resource object node file and the association relationship file, and store the structured data in the graph database.

[0101] In an example embodiment of the present disclosure, the apparatus 600 is further configured to perform word segmentation processing on each type of service text to obtain the first service resource information.

[0102] In an example embodiment of the present disclosure, the apparatus 600 further comprises a training module configured to: extract corresponding triple information based on the second service resource information to form training data; the triple information comprises a head node, a tail node, and an association relationship between the head node and the tail node; perform vectorization processing on the head node, the tail node, and the association relationship in the training data respectively; calculate a loss function value based on the head node corresponding vector, the tail node corresponding vector, and the association relationship corresponding vector; and update the parameters of the head node corresponding vector, the parameters of the tail node corresponding vector, and the parameters of the association relationship corresponding vector based on the loss function value.

[0103] In an example embodiment of the present disclosure, the to-be-analyzed service resource information comprises two of the to-be-analyzed triple node information; the determination module 630 comprises: a vectorization module configured to perform vectorization representation on the two node information in the to-be-analyzed triple node information respectively; a calculation module configured to calculate a corresponding vector of the remaining one node information in the to-be-analyzed triple information based on the two vectorization represented node information using an objective function to obtain a target service resource vector; and a determination sub-module configured to determine the target service resource node information based on the target service resource vector.

[0104] In an example embodiment of the present disclosure, the apparatus 600 further comprises a sending module configured to send the target service resource information to the terminal device to enable the terminal device to display the target service resource information.

[0105] The specific details of each unit or module in the above service resource determination apparatus have been described in detail in the corresponding service resource determination method, and therefore will not be described here.

[0106] Reference Figure 7 In the example embodiment, a service resource determination system 700 is also provided, and the system 700 comprises a user terminal 710 and a server end 720.

[0107] The user terminal 710 is configured to input to-be-analyzed service resource information.

[0108] The server end 720 is configured to obtain the to-be-analyzed service resource information and the first service resource information, the to-be-analyzed service resource information comprising service resource node information; construct a graph database based on the first service resource information; and when the service resource node information belongs to the first service resource information, determine corresponding target service resource node information based on the service resource node information and the graph database using a knowledge representation model.

[0109] The specific details of the modules or units in the service resource determination system have been described in detail in the corresponding service resource determination method, and thus will not be described here.

[0110] As another aspect, the present disclosure also provides a computer readable medium, which can be included in the network device described in the above embodiments, or can exist independently without being assembled into the network device. The computer readable medium carries one or more programs, which, when executed by the network device, cause the network device to implement the method in the above embodiments. For example, the network device can implement the steps shown in the above embodiments, etc. Figures 2 to 5

[0111] It should be noted that the computer readable medium shown in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0112] ​Furthermore, in the exemplary embodiments of the present disclosure, a network device capable of implementing the above method is also provided. Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method or a program product. Therefore, various aspects of the present disclosure can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" herein.

[0113] The network device 800 according to such an embodiment of the present disclosure will be described below with reference to Figure 8 Figure 8 The displayed network device 800 is only an example and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0114] As shown in Figure 8 , the network device 800 is in the form of a general computing device. The components of the network device 800 can include, but are not limited to, the above-mentioned at least one processing unit 810, the above-mentioned at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), a display unit 840.

[0115] Among them, the storage unit stores program code, which can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present disclosure described in the above "exemplary method" part of the specification.

[0116] The storage unit 820 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 8201 and / or a cache memory unit 8202, and can further include a read-only memory (ROM) 8203.

[0117] The storage unit 820 can also include program / utility 8204 having a set of (at least one) program modules 8205, such as an operating system, one or more application programs, other programs, and program data, each of which or a combination thereof can include implementation of a network environment.

[0118] The bus 830 can represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0119] ​The network device 800 can also communicate with one or more external devices 870 such as a keyboard or pointing device, a Bluetooth device, or a Universal Serial Bus (USB) device for example. Additionally, the network device 800 can communicate with one or more devices that enable a user to interact with the network device 800, and / or one or more devices that enable the network device 800 to communicate with one or more other computing devices. Such communication can be via an input / output (I / O) interface 850. Further, the network device 800 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via a network adapter 860. As depicted, the network adapter 860 is in communication with the other components of the network device 800 through the bus 830. It should be appreciated that other hardware and / or software modules can be used in conjunction with the network device 800, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0120] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware coupled with software. Accordingly, the technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB, a mobile hard disk, etc.) or a network, and includes a number of instructions to enable a network device to perform the methods according to the embodiments of the present disclosure.

[0121] In addition, the above-described diagrams are only schematic illustrations of the processes included in the methods according to the example embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above-described diagrams do not indicate or limit the time sequence of the processes. In addition, it will also be readily appreciated that the processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0122] It should be noted that although the steps of the methods in the present disclosure are described in a particular order in the accompanying diagrams, this does not require or imply that the steps must be performed in that particular order, or that all of the steps must be performed to achieve the desired results. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into a single step, a single step can be split into multiple steps, and / or the like, all of which should be considered as part of the present disclosure.

[0123] It is to be understood that the disclosure disclosed and defined in this specification extends to all alternative combinations of two or more of the individual features mentioned or evident from the text or drawings. All of these different combinations constitute various alternative aspects of the disclosure. The embodiments of the specification illustrate the best ways known to the inventors of carrying out the disclosure and the application thereof.

Claims

1. A service resource determination method, characterized by, The method comprises: acquiring to-be-analyzed service resource information and first service resource information, the to-be-analyzed service resource information comprising service resource node information; the first service resource information being service resource node information not in an existing service resource node database; constructing a graph database based on the first service resource information; when the service resource node information belongs to the first service resource information, determining corresponding target service resource node information based on the service resource node information and the graph database by using a knowledge representation model.

2. The service resource determination method of claim 1, wherein, The construction of the graph database comprises: acquiring second service resource information; the second service resource information being service resource node information in the existing service resource node database; constructing a service resource object node file and a file of association relationships between nodes based on the first service resource information and the second service resource information; forming structured data based on the service resource object node file and the file of association relationships, and storing the structured data in the graph database.

3. The service resource determination method according to claim 1 or 2, characterized by, The method further comprises: performing word segmentation processing on various types of business text to acquire the first service resource information.

4. The service resource determination method of claim 2, wherein, The training process of the knowledge representation model comprises: extracting corresponding triple information based on the second service resource information to form training data; the triple information comprising a head node, a tail node, and an association relationship between the head node and the tail node; performing vectorization processing on the head node, the tail node, and the association relationship in the training data respectively; calculating a loss function value based on a head node corresponding vector, a tail node corresponding vector, and an association relationship corresponding vector; updating parameters of the head node corresponding vector, parameters of the tail node corresponding vector, and parameters of the association relationship corresponding vector based on the loss function value.

5. The service resource determination method according to claim 1 or 4, characterized by, The to-be-analyzed service resource information comprises two of to-be-analyzed triple node information; and the determination of the corresponding target service resource node information by using the knowledge representation model comprises: performing vectorization representation on two node information in the to-be-analyzed triple node information respectively; calculating a corresponding vector of a remaining one node information in the to-be-analyzed triple information by using an objective function based on the two vectorized node information, to obtain a target service resource vector; determining target service resource node information based on the target service resource vector.

6. The service resource determination method of claim 5, wherein, The method further comprises: sending the target service resource node information to a terminal device, so that the terminal device displays the target service resource node information.

7. A service resource determination apparatus characterized by comprising: The apparatus comprises: a first acquisition module configured to acquire to-be-analyzed service resource information and first service resource information, the to-be-analyzed service resource information comprising service resource node information; the first service resource information being service resource node information not in an existing service resource node database; a construction module configured to construct a graph database based on the first service resource information; a determination module configured to, when the service resource node information belongs to the first service resource information, determine corresponding target service resource node information based on the service resource node information and the graph database by using a knowledge representation model.

8. A service resource determination system, characterized by, The system comprises: A user terminal configured to input service resource information to be analyzed; A server configured to obtain the service resource information to be analyzed and first service resource information, the service resource information to be analyzed comprising service resource node information; construct a graph database based on the first service resource information; and when the service resource node information belongs to the first service resource information, determine corresponding target service resource node information based on the service resource node information and the graph database using a knowledge representation model. The first service resource information is service resource node information that is not in an existing service resource node database.

9. A computer readable medium having stored thereon a computer program, characterized in that The program, when executed by a processor, implements the method of any one of claims 1-6.

10. A network device, comprising: The network device comprises: one or more processors; a storage device configured to store one or more computer programs, which, when executed by the one or more processors, cause the network device to implement the method of any one of claims 1-6.

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