CN117437006A - Business demand information generation method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a business demand information generation method, a business demand information generation device, a business demand information generation computer device, a business demand information generation storage medium and a business demand information generation computer program product, and relates to the technical fields of artificial intelligence and information security, and can be used in the financial and technological fields or other fields. The method comprises the following steps: acquiring a plurality of business entity information of a target business system, and inputting the business entity information into a trained trust relationship prediction model to obtain trust relationship information among the business entities; generating a security trust relationship chain based on the business entity information and trust relationship information between the business entities; and generating business flow information of the target business system based on the security trust relationship chain, and generating business demand information of the target business system based on the business flow information. By adopting the method, the accuracy and the generation efficiency of the generated service demand information can be improved.
Description
Technical Field
The present invention relates to the field of artificial intelligence and information security technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for generating service requirement information.
Background
With the increase of business complexity, business security is gradually attracting attention. Particularly for financial related services, in order to improve service security, the service function implementation process generally involves security trust related processes such as authorization, authentication, etc. between service entities (such as individual clients, merchants, payment institutions, silver-complexes, teller, etc.). The business requirement information (such as business requirement book) contains business process information, which is basic information for developing business systems or business functions, so that the accuracy of the business requirement information is a key for guaranteeing the security of the business systems/functions.
In the related technology, service demand information is usually written by service designers, and security trust related processes in the service function implementation process are easily omitted due to lack of experience of the service designers, so that the accuracy of the service demand information is low, and the service security is affected.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a business requirement information generating method, apparatus, computer device, computer readable storage medium, and computer program product that can improve the accuracy of business requirement information.
In a first aspect, the present application provides a service requirement information generating method. The method comprises the following steps:
acquiring a plurality of business entity information of a target business system, and inputting each business entity information into a trained trust relationship prediction model to obtain trust relationship information among the business entities;
generating a security trust relationship chain based on the business entity information and the trust relationship information between the business entities;
generating service flow information of the target service system based on the security trust relation chain, and generating service demand information of the target service system based on the service flow information.
In one embodiment, the training process of the trust relationship prediction model includes:
acquiring sample entity information and trust relationship information between each sample entity information;
constructing an adjacency matrix based on the sample entity information and trust relationship information between the sample entity information;
and inputting the adjacency matrix into an initial trust relation prediction model for training to obtain a trained trust relation prediction model.
In one embodiment, the trust relationship prediction model includes a graph neural network layer, a max pooling layer, and a relationship generation layer; inputting the information of each service entity into the trained trust relationship prediction model to obtain the trust relationship information between each service entity, wherein the trust relationship information comprises the following steps:
inputting the business entity information into a trained trust relationship prediction model, and extracting the characteristics of the business entity information through a graph neural network layer in the trust relationship prediction model to obtain characteristic vectors;
and carrying out pooling treatment on the feature vectors through the maximum pooling layer, and mapping the pooled feature information through the relation generation layer to obtain trust relation information among the business entity information.
In one embodiment, the generating a secure trust relationship chain based on the business entity information and the trust relationship information between the business entities includes:
and respectively taking the business entity information as nodes of a security trust relationship chain, and generating edges between the nodes based on the trust relationship information between the business entities to obtain the security trust relationship chain.
In one embodiment, after the generating a secure trust relationship chain based on the service entity information and the trust relationship information between the service entities, the method further includes:
and responding to a security trust relationship chain display instruction input by a user, and visually displaying the security trust relationship chain.
In one embodiment, the generating the business process information of the target business system based on the security trust relationship chain includes:
determining trust relation information between target business entity information and each piece of target business entity information according to the security trust relation chain;
generating business process information of the target business system based on the target business entity information, trust relationship information among the target business entity information and business process template information.
In one embodiment, the generating the service requirement information of the target service system based on the service flow information includes:
and generating the service demand information of the target service system based on the service flow information, a preset service demand template and a preset file generation strategy.
In a second aspect, the present application further provides a service requirement information generating device. The device comprises:
the prediction module is used for acquiring a plurality of business entity information of a target business system, inputting the business entity information into the trained trust relationship prediction model and obtaining trust relationship information among the business entities;
the first generation module is used for generating a security trust relationship chain based on the business entity information and the trust relationship information between the business entities;
and the second generation module is used for generating the business process information of the target business system based on the security trust relation chain and generating the business demand information of the target business system based on the business process information.
In one embodiment, the apparatus further comprises:
the acquisition module is used for acquiring sample entity information and trust relationship information between the sample entity information;
the construction module is used for constructing an adjacency matrix based on the sample entity information and trust relation information between the sample entity information;
and the training module is used for inputting the adjacency matrix into the initial trust relation prediction model for training to obtain a trained trust relation prediction model.
In one embodiment, the trust relationship prediction model includes a graph neural network layer, a max pooling layer, and a relationship generation layer; the prediction module is specifically configured to:
inputting the business entity information into a trained trust relationship prediction model, and extracting the characteristics of the business entity information through a graph neural network layer in the trust relationship prediction model to obtain characteristic vectors; and carrying out pooling treatment on the feature vectors through the maximum pooling layer, and mapping the pooled feature information through the relation generation layer to obtain trust relation information among the business entity information.
In one embodiment, the first generating module is specifically configured to:
and respectively taking the business entity information as nodes of a security trust relationship chain, and generating edges between the nodes based on the trust relationship information between the business entities to obtain the security trust relationship chain.
In one embodiment, the apparatus further comprises:
and the display module is used for responding to a safe trust relation chain display instruction input by a user and visually displaying the safe trust relation chain.
In one embodiment, the second generating module is specifically configured to:
determining trust relation information between target business entity information and each piece of target business entity information according to the security trust relation chain; generating business process information of the target business system based on the target business entity information, trust relationship information among the target business entity information and business process template information.
In one embodiment, the second generating module is specifically configured to:
and generating the service demand information of the target service system based on the service flow information, a preset service demand template and a preset file generation strategy.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of the first aspect when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
According to the business demand information generation method, the business demand information generation device, the computer equipment, the storage medium and the computer program product, the trust relationship among business entity information related to the target business system is predicted through the trust relationship prediction model, then the security trust relationship chain is generated based on the business entities and the trust relationship among the business entities, the security trust relationship chain can reflect which business entities have the bidirectional trust relationship, therefore, necessary business flow information related to security trust can be generated based on the security trust relationship chain, the accuracy of the business demand information generated based on the business flow information is higher, and the security of a software system or a software function developed based on the business demand information is more ensured. In addition, the method can automatically generate the business process information and the business demand information related to the security trust, and the efficiency is higher.
Drawings
FIG. 1 is a flow chart of a method for generating business requirement information in one embodiment;
FIG. 2 is a schematic diagram of a secure trust relationship chain in one example;
FIG. 3 is a flow diagram of a training process of the trust relationship prediction model in one example;
FIG. 4 is a flow diagram of obtaining trust relationship information in one embodiment;
FIG. 5 is a schematic diagram of the structure of a trust relationship prediction model in one example;
FIG. 6 is a flow diagram of generating business process information in one embodiment;
FIG. 7 is a block diagram showing a construction of a business requirement information generating apparatus in one embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
First, before the technical solution of the embodiments of the present application is specifically described, a description is first given of a technical background or a technical evolution context on which the embodiments of the present application are based. With the increase of business complexity, business security is gradually attracting attention. In order to design a highly-safe and trusted service system or service function, usually, when a service person designs a service function flow, the service person needs to consider security trust related flows such as authorization, authentication and the like among entities, and form service requirement information (such as a service requirement book) containing service flow information, which is used for developing and developing a corresponding service system or service function. However, as business complexity increases, more business entities are involved, and business designers are likely to fail to accurately identify trust relationships among the business entities due to insufficient experience, so that necessary security trust related processes may be omitted, resulting in lower accuracy of the generated business requirement books, and failure to meet the requirements of the business system/function on security. Based on the background, the applicant provides the service demand information generation method through long-term research and development and experimental verification, and the security trust relation chain is automatically generated by predicting the trust relation among all service entities related to the service system, so that necessary security trust related service flow information is generated based on the security trust relation chain, the accuracy of the service demand information generated based on the service flow information is higher, the security of a software system or software function developed based on the service demand information can be ensured, and the security trust relation chain, the security trust related service flow information and the service demand information can be automatically generated by the method, so that the efficiency is higher. In addition, the applicant has made a great deal of creative effort to find out the technical problems of the present application and to introduce the technical solutions of the following embodiments.
In one embodiment, as shown in fig. 1, a service requirement information generating method is provided, where the method can be applied to a terminal, a server, a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, portable wearable devices, and the like. The embodiment is exemplified by the method applied to the terminal. In this embodiment, the method includes the steps of:
step 101, obtaining a plurality of business entity information of a target business system, and inputting each business entity information into a trained trust relationship prediction model to obtain trust relationship information among each business entity.
In implementation, the target service system refers to a service system to be designed with a functional flow and writing service demand information, and can be a service system in the financial field, such as a mobile phone bank or a part of functions in the mobile phone bank. The service designer can input the information (such as the name, label and other identification information) of each service entity related to the target service system into the terminal, so that the terminal can acquire the information of the service entity of the target service system.
The terminal may then input the business entity information into the trained trust relationship prediction model to predict whether or not there is a trust relationship between the business entities or what kind of trust relationship is specifically present (authorization, authentication, etc.). The trust relationship information between the business entities comprises trust relationship identification information (in the case of a trust relationship) and identification information without the trust relationship. The trust relationship prediction model may be constructed based on a graph neural network model, and examples of model training processes will be provided hereinafter and will not be described in detail.
Step 102, a secure trust relationship chain is generated based on the business entity information and the trust relationship information between the business entities.
In an implementation, the terminal may generate a secure trust relationship chain according to the information of each service entity and the trust relationship information between each service entity, where the secure trust relationship chain is a chain constructed by service entities with a bidirectional trust relationship, and includes a plurality of nodes and edges connecting two nodes, as in the secure trust relationship chain example shown in fig. 2, each node in the secure trust relationship chain represents one service entity, and a trust relationship exists between the service entities represented by the two nodes connected by the edges. It may be appreciated that the security trust relationship chain may include nodes corresponding to some service entities, or may include nodes corresponding to all service entities. For example, if there is no trust relationship between one business entity and other business entities, the generated security trust relationship chain may not include the node corresponding to the business entity. It will be appreciated that fig. 2 is merely an example, and in practical applications, the business entity and trust relationship may be of other kinds, and the constructed secure trust relationship chain may be more complex.
And step 103, generating service flow information of the target service system based on the security trust relationship chain, and generating service demand information of the target service system based on the service flow information.
In an implementation, after the terminal generates the security trust relationship chain, service flow information of the target service system may be generated according to the security trust relationship chain. For example, a machine learning model may be trained to process an input secure trust relationship chain and output business process information, or business process template information may be used to generate corresponding business process information according to a trust relationship between business entities in the secure trust relationship chain. The business process information may reflect a security trust process to be performed in the implementation process of the business function, for example, according to the security trust relationship chain, the business entity "individual user" and the business entity "third party payment mechanism" have an authentication relationship (a trust relationship), and the business entity "merchant" and the business entity "third party payment mechanism" have an authorization relationship, so the corresponding business process information may be: the merchant authorizes the third party payment mechanism to authenticate the individual user and determine the identity of the user. Then, the terminal can automatically fill the business process information into a requirement description part of the business requirement information, and the business requirement information of the target business system can be generated.
Alternatively, the secure trust relationship chain may comprise a plurality of sub-chains, such as in the example shown in fig. 2, a sub-chain of individual customers, merchants, and third party payees, a sub-chain of teller, individual customers, third party payees, a sub-chain of individual customers, third party payees, a silver-tie, and so forth. And multiple trust relationships may exist between two entities, such as three trust relationships of authorization, authentication, and authentication may exist between an individual customer and a merchant. Thus, multiple candidate business process information may be generated based on different trust relationships between sub-chains and/or business entities in the secure trust relationship chain. The terminal may display the plurality of candidate business process information to enable the user to select the target business process information. Then, the terminal may use the target business process information selected by the user as business process information for generating the business requirement information in response to a selection instruction of the user.
In this embodiment, the trust relationship between the service entity information related to the target service system is predicted by the trust relationship prediction model, and then a security trust relationship chain is generated based on the trust relationship between each service entity and each service entity, and the security trust relationship chain can reflect which service entities have a bidirectional trust relationship, so that the necessary security trust related service flow information can be generated based on the security trust relationship chain, the accuracy of the service requirement information generated based on the service flow information is higher, and the security of the software system or software function developed based on the service requirement information is more ensured. In addition, the method can automatically generate the business process information and the business demand information related to the security trust, and the efficiency is higher.
In one example, as shown in FIG. 3, the training process of the trust relationship prediction model includes the steps of:
step 301, obtaining trust relationship information between sample entity information and each sample entity information.
In implementation, a user may input a trust relationship between a sample entity and each sample entity at a terminal, so that the terminal may obtain trust relationship information between sample entity information and each sample entity information. Or the terminal can read the main system person information (i.e. sample entity information) stored in the service architecture database, such as people's bank, outside-operator entity, and in-line entity such as teller, marketing manager, etc., and can extract trust relationship between each sample entity from service requirement books corresponding to other service systems, thereby obtaining trust relationship information between sample entity information and each sample entity information.
Step 302, constructing an adjacency matrix based on the sample entity information and trust relationship information between each sample entity information.
In an implementation, the terminal may construct the adjacency matrix according to the sample entity information and trust relationship information between each sample entity information. For example, if the sample entity information includes an individual customer (label may be written as 0), a third party payment mechanism (label may be written as 1), a merchant (label may be written as 2), a teller (label may be written as 3), and a silver-back (label may be written as 4). Then the following adjacency matrix can be constructed:
if there is a trust relationship between two entities, the value of the corresponding position in the adjacency matrix is 1, and if there is no trust relationship, the value is 0. For example, if there is a trust relationship between the merchant (0) and the third party payment mechanism (1), then the value of the corresponding location (0, 1) in the adjacency matrix is 1. In other examples, different values may also be used to represent different types of trust relationships, such as 0 for the authorization relationship, 1 for the authentication relationship, and 2 for the authentication relationship. From this adjacency matrix a complex network, i.e. a graph containing nodes and edges, can be generated.
Step 303, inputting the adjacency matrix into the initial trust relation prediction model for training, and obtaining the trained trust relation prediction model.
In implementation, the trust relationship prediction model may be constructed by using a graph neural network, and the terminal may input the adjacency matrix constructed in step 302 to the initial trust relationship prediction model to perform iterative training, so that the trained model can accurately predict the trust relationship between the entities by optimizing model parameters.
The embodiment provides a training process example of a trust relationship prediction model, and the trained trust relationship prediction model can accurately predict whether trust relationship exists among business entities.
In one embodiment, the trust relationship prediction model includes a graph neural network layer, a max pooling layer, and a relationship generation layer. As shown in fig. 4, the process of predicting trust relationship information between service entities in step 101 includes the following steps:
and step 401, inputting the information of each business entity into the trained trust relationship prediction model, and extracting the characteristics of the information of each business entity through a graph neural network layer in the trust relationship prediction model to obtain a characteristic vector.
In implementation, the structure of the trust relationship prediction model may be as shown in FIG. 5, including a graph neural network layer (GCN layer), a max-pooling layer, and a relationship generation layer. The layer of the graphic neural network can be 1 layer or multiple layers (such as 2 layers). The terminal can input the information of each service entity into the trained trust relation prediction model, and the characteristic extraction is carried out on the information of each service entity through the graphic neural network layer to obtain a characteristic vector. Specifically, feature extraction may be performed by the following graph convolution operation:
wherein h is i For the eigenvector of business entity i (complex network or node i in the graph), W is the weight (can be set to 1), A ij For the value of the (i, j) position in the adjacency matrix, if there is an edge between nodes i and j (i.e. there is a trust relationship between business entity i and business entity j), then A ij =1, if there is no edge, a ij ＝0。
And step 402, pooling the feature vectors through a maximum pooling layer, and mapping the pooled feature information through a relationship generation layer to obtain trust relationship information among the business entity information.
In implementation, the terminal may input the feature vector of each service entity output by the neural network layer to the maximum pooling layer for pooling, and then input the pooled feature information output by the maximum pooling layer to the relationship mapping layer to obtain trust relationship information between each service entity. The relationship-generating layer may employ a feedforward neural network (FFNN).
In this embodiment, by including the trust relationship prediction model of the graph neural network layer, the max pooling layer and the relationship generation layer, the trust relationship between each service entity can be accurately predicted, and then a secure trust relationship chain can be automatically constructed for subsequent generation of service flow information and service demand information containing necessary secure trust flow, so that accuracy of service demand information and security of service systems/functions can be improved.
In one embodiment, the process of the secure trust relationship chain in step 102 specifically includes the steps of: and respectively taking the information of each business entity as nodes of the security trust relationship chain, and generating edges between the nodes based on the trust relationship information between the business entities to obtain the security trust relationship chain.
In implementation, after predicting trust relationship information between service entities, the terminal may take each service entity as a node, and connect other nodes having trust relationship with the node by adopting edges to construct a secure trust relationship chain.
In one embodiment, after step 102 generates the secure trust relationship chain, the method further comprises the steps of: and responding to a security trust relationship chain display instruction input by a user, and visually displaying the security trust relationship chain.
In an implementation, a user may trigger a security trust relationship chain to display an instruction at the terminal, for example, a display interface of the terminal may display a related button, the user may click on the related button to trigger the instruction, and the terminal may respond to the instruction to visually display the generated security trust relationship chain. For example, a secure trust relationship chain as shown in FIG. 2 may be displayed.
In this embodiment, the security trust relationship chain is visually displayed, so that service designers can accurately design the service flow related to the security trust according to the trust relationship among the service entities reflected by the security trust relationship chain. And the service personnel can also check and correct the service flow information in the generated service demand information according to the security trust relationship chain so as to further improve the accuracy of the service demand information and the security of the service system.
In one embodiment, as shown in fig. 6, the process of generating the business process information in step 103 specifically includes the following steps:
and step 601, determining the target business entity information and trust relationship information between each target business entity information according to the security trust relationship chain.
In implementation, the target service entity information may be all or part of the service entities included in the secure trust relationship chain, and may be the service entities included in each sub-chain in the secure trust relationship chain. The sub-chain may be composed of two service entities and edges with connected edges, or may be composed of multiple service entities and edges connected step by step (e.g., solid a and B, B and C). The terminal can respectively use the service entity and the trust relationship contained in each sub-chain as a group of target service entity information and trust relationship information, and can also use the service entity and the trust relationship contained in the sub-chain with the longest chain as target service entity information and trust relationship information.
Step 602, generating business process information of the target business system based on the target business entity information, trust relationship information among the target business entity information and business process template information.
In the implementation, the business process template information can be preset, and the terminal can replace corresponding characters in the business process template information with the target business entity information and the trust relationship information to generate the business process information of the target business system. The business process template information can be multiple, and the corresponding relation between the business process template information and the number of business entities and/or the trust relation type can be established, so that the corresponding business process template information can be queried based on the number of target business entity information and/or the trust relation information among the target business entity information, and the business process information can be further generated. It may be appreciated that if multiple sets of target business entity information and trust relationship information are determined in step 601, corresponding business process information may be determined for each set of target business entity information and trust relationship information. Then, the business requirement information can be generated based on all the business process information, and all the generated business process information can be displayed for the user to select. The terminal may generate service requirement information based on the user-selected service flow information.
In this embodiment, according to the target service entity and the trust relationship in the security trust relationship chain, corresponding service flow information is automatically generated, and further service demand information is generated based on the service flow information, so that accuracy of the service demand information and security of a service system can be improved.
In one embodiment, the process of generating the service requirement information in step 103 specifically includes the following steps: and generating service demand information of the target service system based on the service flow information, the preset service demand template and the preset file generation strategy.
In implementation, the terminal may add the generated business process information to the target location (e.g., the requirement description portion) in the business requirement template, so as to automatically generate the business requirement information.
In this embodiment, by automatically generating the security trust relationship chain, the service flow information and the service requirement information, the generation efficiency and accuracy of the service requirement information can be improved, and further, the security of the service system is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a service requirement information generating device for implementing the service requirement information generating method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the one or more service requirement information generating devices provided below may refer to the limitation of the service requirement information generating method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 7, there is provided a service requirement information generating apparatus 700, including: a prediction module 701, a first generation module 702, and a second generation module 703, wherein:
the prediction module 701 is configured to obtain a plurality of service entity information of the target service system, and input each service entity information to the trained trust relationship prediction model to obtain trust relationship information between each service entity.
The first generation module 702 is configured to generate a secure trust relationship chain based on the business entity information and trust relationship information between the business entities.
The second generating module 703 is configured to generate service flow information of the target service system based on the security trust relationship chain, and generate service requirement information of the target service system based on the service flow information.
In one embodiment, the apparatus further comprises an acquisition module, a construction module, and a training module, wherein:
the acquisition module is used for acquiring the sample entity information and trust relationship information between each sample entity information.
And the construction module is used for constructing an adjacency matrix based on the sample entity information and the trust relationship information among the sample entity information.
And the training module is used for inputting the adjacency matrix into the initial trust relation prediction model for training to obtain a trained trust relation prediction model.
In one embodiment, the trust relationship prediction model includes a graph neural network layer, a max-pooling layer, and a relationship generation layer. The prediction module 701 is specifically configured to: inputting the information of each business entity into a trained trust relationship prediction model, and extracting the characteristics of the information of each business entity through a graph neural network layer in the trust relationship prediction model to obtain a characteristic vector; and carrying out pooling treatment on the feature vectors through a maximum pooling layer, and mapping the pooled feature information through a relation generation layer to obtain trust relation information among the business entity information.
In one embodiment, the first generating module 702 is specifically configured to: and respectively taking the information of each business entity as nodes of the security trust relationship chain, and generating edges between the nodes based on the trust relationship information between the business entities to obtain the security trust relationship chain.
In one embodiment, the apparatus further comprises a display module for visually displaying the secure trust relationship chain in response to a user entered secure trust relationship chain display instruction.
In one embodiment, the second generating module 703 is specifically configured to: determining trust relationship information between target business entity information and each target business entity information according to the security trust relationship chain; generating business flow information of the target business system based on the target business entity information, trust relation information among the target business entity information and business flow template information.
In one embodiment, the second generating module 703 is specifically configured to: and generating service demand information of the target service system based on the service flow information, the preset service demand template and the preset file generation strategy.
The above-described respective modules in the service demand information generating apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a business need information generation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
The business demand information generation method, device, computer equipment, storage medium and computer program product provided by the application relate to the technical field of artificial intelligence and information security, can be used in the financial and technological field or other fields, and are not limited in application field.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (11)
1. A method for generating service requirement information, the method comprising:
acquiring a plurality of business entity information of a target business system, and inputting each business entity information into a trained trust relationship prediction model to obtain trust relationship information among the business entities;
generating a security trust relationship chain based on the business entity information and the trust relationship information between the business entities;
generating service flow information of the target service system based on the security trust relation chain, and generating service demand information of the target service system based on the service flow information.
2. The method of claim 1, wherein the training process of the trust relationship prediction model comprises:
acquiring sample entity information and trust relationship information between each sample entity information;
constructing an adjacency matrix based on the sample entity information and trust relationship information between the sample entity information;
and inputting the adjacency matrix into an initial trust relation prediction model for training to obtain a trained trust relation prediction model.
3. The method of claim 1, wherein the trust relationship prediction model comprises a graph neural network layer, a max pooling layer, and a relationship generation layer; inputting the information of each service entity into the trained trust relationship prediction model to obtain the trust relationship information between each service entity, wherein the trust relationship information comprises the following steps:
inputting the business entity information into a trained trust relationship prediction model, and extracting the characteristics of the business entity information through a graph neural network layer in the trust relationship prediction model to obtain characteristic vectors;
and carrying out pooling treatment on the feature vectors through the maximum pooling layer, and mapping the pooled feature information through the relation generation layer to obtain trust relation information among the business entity information.
4. The method of claim 1, wherein generating a secure trust relationship chain based on each of the business entity information and trust relationship information between each of the business entities comprises:
and respectively taking the business entity information as nodes of a security trust relationship chain, and generating edges between the nodes based on the trust relationship information between the business entities to obtain the security trust relationship chain.
5. The method of claim 1, wherein after generating a secure trust relationship chain based on each of the business entity information and trust relationship information between each of the business entities, further comprising:
and responding to a security trust relationship chain display instruction input by a user, and visually displaying the security trust relationship chain.
6. The method of claim 1, wherein the generating business process information for the target business system based on the secure trust relationship chain comprises:
determining trust relation information between target business entity information and each piece of target business entity information according to the security trust relation chain;
generating business process information of the target business system based on the target business entity information, trust relationship information among the target business entity information and business process template information.
7. The method of claim 1, wherein generating business requirement information for the target business system based on the business process information comprises:
and generating the service demand information of the target service system based on the service flow information, a preset service demand template and a preset file generation strategy.
8. A service demand information generating apparatus, characterized by comprising:
the prediction module is used for acquiring a plurality of business entity information of a target business system, inputting the business entity information into the trained trust relationship prediction model and obtaining trust relationship information among the business entities;
the first generation module is used for generating a security trust relationship chain based on the business entity information and the trust relationship information between the business entities;
and the second generation module is used for generating the business process information of the target business system based on the security trust relation chain and generating the business demand information of the target business system based on the business process information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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