CN114186555A

CN114186555A - Demand identification method, apparatus, electronic device, medium, and computer program

Info

Publication number: CN114186555A
Application number: CN202111513855.0A
Authority: CN
Inventors: 高明; 丁诗璟; 沈文俊; 万聪; 苏蜜; 余刚; 李亮; 沈冰华; 袁园; 姚琛; 陈思广; 刘维安; 谢传聪
Original assignee: CCB Finetech Co Ltd
Current assignee: CCB Finetech Co Ltd
Priority date: 2021-12-10
Filing date: 2021-12-10
Publication date: 2022-03-15

Abstract

The present disclosure provides a demand identification method, apparatus, electronic device, medium, and computer program. The project demand identification method based on the knowledge graph comprises the following steps: acquiring historical project data, wherein the historical project data comprises project information, implementation object information and attribution field information; constructing a knowledge graph according to the project information, the implementation object information and the attribution field information, wherein the knowledge graph comprises nodes and edges; acquiring a new requirement document; and performing project requirement identification on the requirement document based on the knowledge graph. The method can realize automatic distribution and automatic identification of new project requirements, reduce the workload of requirement accepting personnel, improve the requirement processing efficiency and the user experience, and reduce the error rate. In addition, the method and the system can effectively enable the historical data to provide basis for new project decision-making, reduce project establishment difficulty, reduce project design and audit difficulty, contribute to improving project establishment efficiency, prevent repeated investment of projects and reduce project investment cost of management and control enterprises.

Description

Demand identification method, apparatus, electronic device, medium, and computer program

Technical Field

The present disclosure relates to the field of big data technologies, and more particularly, to a demand identification method, apparatus, electronic device, medium, and computer program.

Background

In real life, project operation flows are as follows: 1) compiling and proposing requirements; 2) a demand analyst claims the demand; 3) the demand analyst identifies demand content and maturity; 4) forwarding the requirement for the claim error to the correct handler; 5) returning directly to immature requirements; 6) further identifying the necessity for project implementation with respect to a claimed and mature need; 7) technical personnel design an architecture scheme and analyze feasibility; 8) evaluating project input cost and output benefit, and preparing a project establishment material; 9) submitting the standing materials to a leader for examination and review; 10) project review (offline). The existing flow needs to consume a large amount of labor cost. The method has important dependence on the experience of project processing personnel, if the condition of personnel adjustment occurs, a long period is needed to enable a new person to take over related services, and the evaluation of various materials in the project establishment process is completely dependent on the experience.

Disclosure of Invention

In view of the above, the present disclosure provides a knowledge-graph-based project requirement identification method, apparatus, electronic device, computer-readable storage medium, and computer program with high project establishment efficiency, low error rate, and low project cost.

One aspect of the present disclosure provides a project requirement identification method based on a knowledge graph, including: acquiring historical project data, wherein the historical project data comprises project information, implementation object information and attribution field information; constructing a knowledge graph according to the project information, the implementation object information and the attribution domain information, wherein the knowledge graph comprises nodes and edges; acquiring a new requirement document; and performing project requirement identification on the requirement document based on the knowledge graph.

According to the knowledge graph-based project requirement identification method disclosed by the embodiment of the disclosure, by establishing the knowledge graph and matching the requirement keywords in the requirement document with the knowledge graph, the automatic distribution and automatic identification of new project requirements can be realized, the workload of requirement acceptors is reduced, the requirement processing efficiency and the user experience are improved, and the error rate is reduced. In addition, the method and the system can effectively enable the historical data to provide basis for new project decision-making, reduce project establishment difficulty, reduce project design and audit difficulty, help to improve project establishment efficiency, prevent repeated investment of projects, and help to effectively control project investment cost of enterprises.

In some embodiments, the project information includes a project name and a project event, where the project event includes m implementation objects implementing the project, m is an integer greater than or equal to 1, the implementation object information includes an object name and an object business domain, the domain includes a domain keyword and a keyword weight corresponding to the domain keyword, and the constructing the knowledge graph according to the project information, the implementation object information, and the domain information includes: and constructing a knowledge graph by taking the project name, the object name and the domain key word as nodes and taking the project event and the object service domain as edges.

In some embodiments, the project information further includes at least one of project period, project cost, project population, project manager, and project status.

In some embodiments, said identifying the need for the project for the need document based on the knowledge-graph comprises: extracting requirement keywords in the requirement document; calculating the requirement weight of the requirement key words in the requirement document; matching the requirement keywords with the requirement weight being more than or equal to the threshold weight with the knowledge graph; and taking the attribution domain corresponding to the domain keyword which is the same as or similar to the requirement keyword in the knowledge graph as the requirement domain of the requirement document.

In some embodiments, the calculating the requirement weight of the requirement keyword in the requirement document comprises: calculating the occurrence frequency of the requirement keywords in the requirement document; calculating the total number of words in the requirement document; and dividing the occurrence frequency of the requirement keywords in the requirement document by the total number of terms in the requirement document to obtain the requirement weight.

Another aspect of the present disclosure provides a knowledge-graph-based project requirement identification apparatus, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical project data, and the historical project data comprises project information, implementation object information and attribution field information; a construction module for constructing a knowledge graph according to the project information, the implementation object information, and the attribution domain information, the knowledge graph including nodes and edges; the second acquisition module is used for acquiring a new requirement document; and the identification module is used for carrying out project requirement identification on the requirement document based on the knowledge graph.

Another aspect of the present disclosure provides an electronic device comprising one or more processors and one or more memories, wherein the memories are configured to store executable instructions that, when executed by the processors, implement the method as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

fig. 1 schematically illustrates an exemplary system architecture to which the methods, apparatus, and methods may be applied, in accordance with an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow diagram of a knowledge-graph based project requirement identification method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow diagram for building a knowledge-graph from project information, implementation object information, and home domain information, according to an embodiment of the disclosure;

FIG. 4 schematically shows a schematic diagram of a knowledge-graph according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow diagram for project requirement identification of a requirement document based on a knowledge-graph according to an embodiment of the disclosure;

FIG. 6 schematically illustrates a flow chart for calculating a demand weight for a demand keyword in a demand document according to an embodiment of the present disclosure;

FIG. 7 schematically illustrates a block diagram of a knowledge-graph based project requirement identification apparatus, in accordance with an embodiment of the present disclosure;

FIG. 8 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure. In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features.

Fig. 1 schematically illustrates an exemplary system architecture 100 to which a knowledge-graph based project requirement identification method, a project requirement identification apparatus, an electronic device, a computer-readable storage medium, and a computer program may be applied, according to embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

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

The user may use the

terminal devices

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

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the item requirement identification method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the item requirement identification apparatus provided by the embodiment of the present disclosure may be generally disposed in the server 105. The project requirement identification method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the item requirement identification apparatus provided in the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

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

The project requirement identification method of the embodiment of the present disclosure will be described in detail below through fig. 2 to 6 based on the scenario described in fig. 1.

FIG. 2 schematically illustrates a flow chart of a knowledge-graph based project requirement identification method according to an embodiment of the present disclosure.

As shown in fig. 2, the item requirement identification method of the embodiment includes operations S210 to S240.

In operation S210, history item data is acquired, wherein the history item data includes item information, implementation object information, and home domain information.

In operation S220, a knowledge graph is constructed according to the project information, the implementation object information, and the home domain information, the knowledge graph including nodes and edges. As a possible implementation manner, the project information may include a project name and a project event, where the project event includes m implementation objects implementing the project, m is an integer greater than or equal to 1, the implementation object information includes an object name and an object service domain, and the attribution domain includes a domain keyword and a keyword weight corresponding to the domain keyword.

For example, company A needs to create a new office, entrusts company B to build a building, entrusts company C to make an office chair, entrusts company D to develop a business system, and here, the project name is the new office; the project event is entrusted with B company to take a building, entrusted with C company to make an office chair and entrusted with D company to develop a business system; the object name is company B, and the object business field corresponding to the company B is the building field; the object name is also C company, and the object business field corresponding to the C company is a furniture field; the object name is also company D, and the object business field corresponding to the company D is the Internet field; the domain keyword of the belonging domain corresponding to the project information and the keyword weight corresponding to the domain keyword are (civil engineering, 80%), (construction, 70%), (furniture, 20%), (computer, 20%) and (internet, 20%), respectively.

As one implementable manner, as shown in fig. 3, the operation S220 of constructing the knowledge-graph according to the project information, the implementation object information, and the home domain information includes an operation S221.

In operation S221, a knowledge graph is constructed with the project name, the object name, and the domain keyword as nodes, and the project event and the object business domain as edges. In conjunction with fig. 4, the knowledge graph may use the project name "new office", the object names "B company", "C company", and "D company" and the home domain "(civil engineering, 80%), (construction, 70%), (furniture, 20%), (computer, 20%) and (internet, 20%)" as nodes, establish an edge with the project event between the project name and the object name, establish an edge with the relationship between the project name and the home domain, and establish an edge with the object business domain between the object name and the home domain. Thus, establishing the knowledge-graph may be facilitated through operation S221.

In operation S230, a new requirement document is obtained, wherein the new requirement document can be understood as a document of a new project requiring project requirement identification.

In operation S240, a project requirement identification is performed on the requirement document based on the knowledge-graph. As one way of implementation, as shown in FIG. 5, the identifying of the item requirement for the requirement document by operation S240 based on the knowledge-graph includes operations S241-S244.

In operation S241, a requirement keyword in a requirement document is extracted, wherein the requirement keyword may be multiple.

In operation S242, calculating the requirement weight of the requirement keyword in the requirement document, for example, extracting the requirement keyword a, the requirement keyword B and the requirement keyword C from the requirement document, and in conjunction with fig. 6, calculating the requirement weight of the requirement keyword in the requirement document in operation S242 may include operations S2421 to S2423.

In operation S2421, the number of occurrences of the demand keyword in the demand document is calculated, for example, the number of occurrences of the demand keyword a, the demand keyword B, and the demand keyword C in the demand document are calculated, respectively.

In operation S2422, the total number of words in the requirement document is calculated.

In operation S2423, the number of occurrences of the requirement keyword in the requirement document is divided by the total number of words in the requirement document to obtain a requirement weight of the requirement keyword in the requirement document.

In operation S243, the requirement keywords having the requirement weights greater than or equal to the threshold weight are matched with the knowledge graph. For example, a threshold weight may be set, the requirement weight of each requirement keyword is compared with the threshold weight, a requirement weight greater than or equal to the threshold weight is selected, and the requirement keyword corresponding to the requirement weight greater than or equal to the threshold weight is matched with the knowledge graph, that is, matched with the attribution field in the knowledge graph.

In operation S244, the attribution domain corresponding to the domain keyword in the knowledge graph, which is the same as or similar to the requirement keyword, is used as the requirement domain of the requirement document.

In some embodiments of the present disclosure, the project information may further include at least one of a project period, a project cost, a number of projects, a project manager, and a project status. The constructed knowledge graph can be richer in content by taking information such as project period, project cost, project number, project manager and project state as project information, so that more useful information can be identified when project requirement identification is carried out based on the knowledge graph, and reference are provided for new projects.

The following detailed description of a knowledge graph according to embodiments of the present disclosure is provided with the understanding that the following description is illustrative only and not intended to be a specific limitation of the present disclosure.

It is understood that the logical structure of the knowledge-graph is divided into two levels: a data layer and a mode layer. In the data layer of the knowledge graph, if data takes 'entity-relationship-entity' or 'entity-attribute-value' as a basic expression mode, and we refer to the expression mode as 'triple', all data stored in a graph database form a huge entity relationship network to form the knowledge graph. In the present disclosure, the project name "new office place", the object names "B company", "C company", and "D company", and the home field "(civil engineering, 80%), (building, 80%), (construction, 70%), (furniture, 20%), (computer, 20%), (internet, 20%)" are entities; the relation between the project name and the object name is relationship, the relation between the project name and the attribution field is relationship, and the relation between the object name and the attribution field is relationship.

The schema layer is above the data layer and is the core of the knowledge graph, the schema layer stores the abstracted knowledge, the ontology is usually adopted to manage the schema layer of the knowledge graph, and the ontology base is used to support the axiom, the rule and the constraint condition to standardize the relationship between the entity, the relationship and the type and the attribute of the entity. The position of the ontology base in the knowledge map is equivalent to that of a mould of the knowledge base, and the knowledge base with the ontology base has less redundant knowledge.

Another important concept herein related to knowledge-graphs is "ontologies". The concept of ontologies was originally originated in the field of philosophy and refers to the interpretation and explanation of objective presence systems. An ontology is actually a formalized representation of a set of concepts and their relationships in a particular domain. Specifically to the field of project requirement identification, the ontology aims to classify knowledge terms identified by project requirements and specify the relationship among various classifications and their own attributes.

The ontology can be manually constructed in a manual editing mode (by means of ontology editing software) or can be constructed in a data-driven automatic mode. There are many methods for constructing the ontology and the knowledge graph, and a rough knowledge graph construction process in the practical work is described here, which includes operation S001 to operation S003.

At operation S001, an ontology base is constructed, and as mentioned above, the first task of constructing the project requirement identification domain knowledge graph is to construct an ontology model, that is, to define the general concepts of the industry as entities and the relationships between the entities.

The most central subjects of project requirement identification are project names of "new office places", object names of "B company", "C company" and "D company" and home areas "(civil engineering, 80%), (construction, 70%), (furniture, 20%), (computer, 20%), (internet, 20%)" and the like. After the ontology is built, the ontology needs to be verified by comparing with the actual service, so that the ontology can correctly describe the current service and all the service flows are included.

In operation S002, a knowledge graph is constructed, which is a prerequisite for graph application, and the main task of construction is to extract data from different data sources according to rules specified by the ontology model. For vertical domain knowledge graphs, the main source of data is the data of the business itself, which is usually the organization's own private data stored in a structured form. And (4) extracting and converting the data into map data through ETL processing. The storage form of map data currently includes storage based on RDF and the like and database storage.

It can be understood that the RDF graph database is used for storing the triad nodes and the relations according with the W3C standard graph, so that traversal and expansion are convenient, and the standard inference engine has the advantages of high portability of transaction management data, high engineering degree and good visualization effect.

In practical engineering application, a knowledge graph is mainly stored in a graph database mode, the graph database which is popular at present is Neo4j, the graph database and Neo4j are not described in detail in this document, Neo4j provides various data loading modes, and small-scale data (1 w-10 w pieces of data) can be loaded in a CSV mode.

The system implementation principle is as follows:

1) the system combines the historical project condition, extracts project entities, constructs a project archive knowledge graph, and forms a mesh association graph of project names, project events, attribution fields and the like.

2) After a business department demand person puts forward a demand, the system analyzes the business field to which the demand belongs and the demand maturity by combining a knowledge map and the demand situation of a historical project put on production, and returns the demand with lower maturity to the business department for refinement and then puts forward; and aiming at the requirements with higher maturity, the requirements are automatically distributed to the requirements analysts in the related field.

3) A field demand analyst accepts demands, analyzes whether existing products can meet the current demands or not by combining historical project conditions, and evaluates project necessity.

4) And submitting the implementable project to a technical responsible person for architecture design and cost estimation, and providing historical project conditions for a related decision maker to refer to by the knowledge graph.

5) And auditing the project establishment by the auditor aiming at the early preparation work.

In combination with the principle of the system, the business flow diagram is as follows:

compared with the prior art, the project archive knowledge graph is added, and decision support is provided in the whole process of requirement acceptance and project establishment through the knowledge graph.

The traditional project establishment flow has important dependence on the experience of project processing personnel, if the condition of personnel adjustment occurs, a long period is needed for a new person to take over related services, and the evaluation of various materials in the project establishment process is completely based on the experience. Through the archive knowledge graph, historical data can be effectively used as a basis for new project decision-making, project item establishing difficulty is reduced, project item establishing efficiency is improved, repeated investment of projects is prevented, and project investment cost of an effective management and control enterprise is facilitated.

Based on the above project demand identification method based on the knowledge graph, the present disclosure also provides a project demand identification apparatus 10 based on the knowledge graph. The knowledge-graph-based project requirement identification apparatus 10 will be described in detail below with reference to fig. 7.

Fig. 7 schematically shows a block diagram of the knowledge-graph based project requirement identification apparatus 10 according to an embodiment of the present disclosure.

The knowledge-graph-based project requirement identification device 10 comprises a first acquisition module 1, a construction module 2, a second acquisition module 3 and an identification module 4.

A first obtaining module 1, where the first obtaining module 1 is configured to perform operation S210: acquiring historical project data, wherein the historical project data comprises project information, implementation object information and attribution domain information.

A building block 2, the building block 2 being configured to perform operation S220: and constructing a knowledge graph according to the project information, the implementation object information and the attribution domain information, wherein the knowledge graph comprises nodes and edges.

A second obtaining module 3, where the second obtaining module 3 is configured to perform operation S230: and acquiring a new requirement document.

An identification module 4, the identification module 4 being configured to perform operation S240: and carrying out project requirement identification on the requirement document based on the knowledge graph.

Since the item requirement identification apparatus 10 is configured based on the item requirement identification method, the beneficial effects of the item requirement identification apparatus 10 are the same as those of the item requirement identification method, and are not described herein again.

In addition, according to the embodiment of the present disclosure, any plurality of the first obtaining module 1, the constructing module 2, the second obtaining module 3, and the identifying module 4 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module.

According to an embodiment of the present disclosure, at least one of the first obtaining module 1, the constructing module 2, the second obtaining module 3, and the identifying module 4 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or may be implemented in any one of three implementations of software, hardware, and firmware, or in a suitable combination of any several of them.

Alternatively, at least one of the first obtaining module 1, the building module 2, the second obtaining module 3 and the identifying module 4 may be at least partly implemented as a computer program module, which when executed may perform a corresponding function.

FIG. 8 schematically illustrates a block diagram of an electronic device adapted to implement a demand identification method according to an embodiment of the present disclosure.

As shown in fig. 8, an electronic apparatus 900 according to an embodiment of the present disclosure includes a processor 901 which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. Processor 901 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The processor 901 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 900 may also include input/output (I/O) interface 905, input/output (I/O) interface 905 also connected to bus 904, according to an embodiment of the present disclosure. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The driver 910 is also connected to an input/output (I/O) interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: 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), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 902 and/or the RAM 903 described above and/or one or more memories other than the ROM 902 and the RAM 903.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. The program code is for causing a computer system to perform the methods of the embodiments of the disclosure when the computer program product is run on the computer system. The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 901. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, and downloaded and installed through the communication section 909 and/or installed from the removable medium 911. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A project demand identification method based on a knowledge graph is characterized by comprising the following steps:

acquiring historical project data, wherein the historical project data comprises project information, implementation object information and attribution field information;

constructing a knowledge graph according to the project information, the implementation object information and the attribution domain information, wherein the knowledge graph comprises nodes and edges;

acquiring a new requirement document; and

and carrying out project requirement identification on the requirement document based on the knowledge graph.

2. The knowledge-graph-based project requirement identification method according to claim 1, wherein the project information comprises a project name and a project event, wherein the project event comprises m implementation objects for implementing the project, m is an integer greater than or equal to 1, the implementation object information comprises an object name and an object business field, the home field comprises a field keyword and a keyword weight corresponding to the field keyword,

the constructing a knowledge graph according to the project information, the implementation object information and the attribution domain information comprises:

and constructing a knowledge graph by taking the project name, the object name and the domain key word as nodes and taking the project event and the object service domain as edges.

3. The knowledge-graph-based project requirement identification method of claim 1, wherein the project information further comprises at least one of project period, project cost, project population, project manager and project status.

4. The knowledge-graph-based project requirement identification method according to any one of claims 1-3, wherein the knowledge-graph-based project requirement identification of the requirement document comprises:

extracting requirement keywords in the requirement document;

calculating the requirement weight of the requirement key words in the requirement document;

matching the requirement keywords with the requirement weight being more than or equal to the threshold weight with the knowledge graph; and

and taking the attribution domain corresponding to the domain keyword which is the same as or similar to the requirement keyword in the knowledge graph as the requirement domain of the requirement document.

5. The knowledge-graph-based project requirement identification method according to claim 4, wherein the calculating the requirement weight of the requirement keyword in the requirement document comprises:

calculating the occurrence frequency of the requirement keywords in the requirement document;

calculating the total number of words in the requirement document; and

and dividing the occurrence times of the requirement keywords in the requirement document by the total number of words in the requirement document to obtain the requirement weight.

6. A project requirement identification device based on knowledge graph is characterized by comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical project data, and the historical project data comprises project information, implementation object information and attribution field information;

a construction module for constructing a knowledge graph according to the project information, the implementation object information, and the attribution domain information, the knowledge graph including nodes and edges;

the second acquisition module is used for acquiring a new requirement document; and

an identification module to perform project requirement identification on the requirement document based on the knowledge-graph.

7. An electronic device, comprising:

one or more processors;

one or more memories for storing executable instructions that, when executed by the processor, implement the method of any of claims 1-5.

8. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, implement a method according to any one of claims 1 to 5.

9. A computer program, characterized in that it comprises one or more executable instructions which, when executed by a processor, implement the method according to any one of claims 1 to 5.