CN113535125A - Financial demand item generation method and device - Google Patents

Financial demand item generation method and device Download PDF

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CN113535125A
CN113535125A CN202110891478.8A CN202110891478A CN113535125A CN 113535125 A CN113535125 A CN 113535125A CN 202110891478 A CN202110891478 A CN 202110891478A CN 113535125 A CN113535125 A CN 113535125A
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贝飞
徐伟
刘征彦
王世佳
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The embodiment of the application provides a financial demand item generation method and device, which can be used in the technical field of finance, and the method comprises the following steps: extracting keywords for generating financial demand items in the divided problem data sets based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises a plurality of test problems in a plurality of iteration processes corresponding to development projects; clustering each test problem in the target problem data set to obtain a corresponding key problem word set; and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item. The method and the device can effectively improve the individuation, the accuracy and the reliability of the generated financial demand items, are specially suitable for the financial industry, can effectively improve the automation degree and the efficiency of the financial demand item generation process, and further improve the efficiency and the reliability of online and optimized financial software products according to the financial demand items.

Description

Financial demand item generation method and device
Technical Field
The application relates to the technical field of data processing, in particular to the technical field of finance, and specifically relates to a financial demand item generation method and device.
Background
In the internet era, in order to quickly adapt to business requirements, financial institutions such as banks and the like gradually convert the development process of projects from a traditional mode into agile development so as to adapt to the market requirement of quick online products. Each project consists of a plurality of requirement items, and each requirement item is jointly developed and realized by one or more product applications. In the agile iteration process of the project, a great number of usability problems which do not influence the basic functions of the product are usually generated in the test process, and the problems do not require forced optimization in extremely short iteration time. After the product is popularized, in order to facilitate project management, the original requirement completes the closed loop of a development link, and the original requirement is often manually written into a requirement item of the next iteration project, so that the product is further optimized and continuously updated. The method weakens the value of the test problems discovered in the previous period and also seriously consumes the labor cost of outputting the demand items in the iteration project.
In the aspect of financial demand item output, the current demands can be roughly divided into business transformation demands and technical transformation demands, and the current common method is as follows: for the requirements of financial business transformation, a demand side collects the problems of feedback of customers in using products through an online and offline mode, sums up the problems by combining with the early test problems, and writes demand items manually. For the technical transformation requirements, the demand side artificially writes the technical transformation requirements by seeking opinions of developers and testers and combining with the actual situation of financial commissioning. However, in any type, because each step has extremely high requirement on human cost, the generation efficiency of the financial demand item is too low, and the like, so that the method cannot be completely adapted to the quick process of quickly optimizing products on line on the basis of ensuring the individuation of the financial demand item.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a financial demand item generation method and device, which can effectively improve the individuation, accuracy and reliability of the generated financial demand item, are specially suitable for the financial industry, can effectively improve the automation degree and efficiency of the financial demand item generation process, and further improve the efficiency and reliability of online and optimized financial software products according to the financial demand item.
In order to solve the technical problem, the application provides the following technical scheme:
in a first aspect, the present application provides a financial requirement item generating method, including:
extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects;
clustering each test problem in the target problem data set to obtain a corresponding key problem word set;
and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item.
Further, before extracting keywords for generating a financial demand item in the segmented question data set based on the preset financial vocabulary set, the method further comprises:
obtaining test problems in a plurality of iteration processes corresponding to each development project, and generating a corresponding problem data set;
preprocessing the problem data set;
and performing word segmentation on the preprocessed problem data set based on a preset conditional random field CRF algorithm to obtain a word-segmented problem data set.
Further, the preprocessing the problem data set includes:
performing data cleaning on the problem data set;
formatting a problem data set after data cleaning so that the problem data set comprises a unique identifier of each test problem and a corresponding relation between test problem contents, wherein the test problem contents are divided by attributes, and each attribute comprises: project name, service category, service scenario, problem description and related applications.
Further, the extracting of the keywords for generating the financial demand items in the segmented question data sets based on the preset financial vocabulary sets comprises:
calling a preset financial vocabulary set;
extracting financial vocabularies used for generating financial demand items in the financial vocabulary set from the divided word problem data set;
and labeling the part of speech of each financial vocabulary by using a preset FudanNLP tool kit, and extracting the financial vocabulary of which the part of speech is noun and verb as a keyword to form a target problem data set consisting of each keyword.
Further, before clustering each test question in the target question data set to obtain a corresponding keyword set, the method further includes:
dividing keywords corresponding to each test problem in the target problem data set into a professional noun, a non-professional noun, a professional verb and a non-professional verb based on a preset professional vocabulary division rule;
and respectively carrying out weight assignment on the keywords belonging to the professional nouns, the non-professional nouns, the professional verbs and the non-professional verbs according to the sequence of the weighted values from large to small.
Further, the clustering each test question in the target question data set to obtain a corresponding keyword set further includes:
and clustering each test problem in the target problem data set after the keyword weight assignment based on a preset LP clustering algorithm to obtain a corresponding keyword set.
Further, still include:
and outputting the financial demand item.
In a second aspect, the present application provides a financial demand item generating apparatus, including:
the system comprises a data extraction module, a data analysis module and a data analysis module, wherein the data extraction module is used for extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set so as to obtain a target problem data set consisting of the keywords, and the problem data set comprises a plurality of test problems in a plurality of iteration processes corresponding to development projects; the data clustering module is used for clustering each test problem in the target problem data set to obtain a corresponding keyword set; and the template generating module is used for inputting the keyword set into a preset financial demand item text template so as to generate a corresponding financial demand item.
In a third aspect, the present application provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the financial requirement item generating method.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the financial demand item generation method.
According to the technical scheme, the financial demand item generating method and device provided by the application comprise the following steps: extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects; clustering each test problem in the target problem data set to obtain a corresponding key problem word set; inputting the key problem word set into a preset financial demand item character template to generate a corresponding financial demand item, and extracting keywords for generating the financial demand item from the divided word problem data set based on a preset financial vocabulary set, so that the target problem data set can be specially suitable for the financial industry on the basis of effectively reducing the data volume of the divided word problem data set, and the reliability, the accuracy and the applicability of subsequent clustering processing on the target problem data set can be effectively improved; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a relationship between a financial demand item generation apparatus and a client device in an embodiment of the present application.
Fig. 2 is a first flowchart of a financial demand item generation method in an embodiment of the present application.
Fig. 3 is a second flowchart of the financial demand item generation method in the embodiment of the present application.
Fig. 4 is a third flowchart of the financial demand item generation method in the embodiment of the present application.
Fig. 5 is a fourth flowchart illustrating a financial demand item generation method according to an embodiment of the present application.
Fig. 6 is a fifth flowchart illustrating a financial demand item generating method according to an embodiment of the present application.
Fig. 7 is a sixth flowchart illustrating a financial demand item generating method according to an embodiment of the present application.
Fig. 8 is a seventh flowchart illustrating a financial demand item generating method according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of a financial demand item generation apparatus in the embodiment of the present application.
Fig. 10 is a schematic structural diagram of a financial demand item generation system provided in an application example of the present application.
Fig. 11 is a schematic structural diagram of a data preprocessing apparatus provided in an application example of the present application.
Fig. 12 is a schematic structural diagram of a cluster analysis apparatus according to an application example of the present application.
Fig. 13 is a schematic structural diagram of a requirement item generating apparatus provided in an application example of the present application.
Fig. 14 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the method and apparatus for generating financial demand items disclosed in the present application can be used in the field of financial technology, and can also be used in any field other than the field of financial technology.
Aiming at the problems that the existing financial demand item generation mode cannot meet the requirement of financial demand item generation efficiency on the basis of ensuring the individuation of the financial demand item, and the like, namely, in the output process of the demand item, no method capable of automatically generating the demand item without manpower exists, and how to find a solution capable of automatically summarizing and generating the demand item from the known problems is a technical problem to be solved urgently in the field; the embodiment of the application provides a financial demand item generation method, which comprises the steps of extracting keywords for generating a financial demand item in a word-divided problem data set based on a preset financial vocabulary set so as to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects; clustering each test problem in the target problem data set to obtain a corresponding key problem word set; inputting the key problem word set into a preset financial demand item character template to generate a corresponding financial demand item, and extracting keywords for generating the financial demand item from the divided word problem data set based on a preset financial vocabulary set, so that the target problem data set can be specially suitable for the financial industry on the basis of effectively reducing the data volume of the divided word problem data set, and the reliability, the accuracy and the applicability of subsequent clustering processing on the target problem data set can be effectively improved; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
In one or more embodiments of the present application, the conditional Random field crf (conditional Random fields) algorithm is a conditional probability distribution model of another set of output sequences given a set of input sequences, and is widely used in natural language processing.
In one or more embodiments of the present application, the FudanNLP toolkit is a toolkit developed for chinese natural language processing, and also contains machine learning algorithms and datasets to accomplish these tasks. The method comprises the functions of Chinese word segmentation, part of speech tagging, named entity identification, dependency syntactic analysis, keyword extraction, time phrase identification, text classification, news clustering, hierarchical classification, online learning and the like.
In one or more embodiments of the present application, an LP (Layer-Partition) clustering algorithm is based on the idea of partitioning and hierarchical clustering, and the cluster distance is calculated each time, and the current optimal solution is found depending on the last calculation result, thereby avoiding comparing the similarity between all clusters, and improving the overall clustering speed.
Based on the above, the present application further provides a financial requirement item generating device for implementing the financial requirement item generating method provided in one or more embodiments of the present application, where the financial requirement item generating device may be a server, see fig. 1, the financial requirement item generating device may be sequentially connected to each client device through a third-party server or the like in a communication manner, the financial requirement item generating device may receive a financial requirement item generating request sent by the client device, and extract keywords used for generating a financial requirement item in a participled problem data set based on a preset financial vocabulary set to obtain a target problem data set composed of the keywords, where the problem data set includes test problems in a plurality of iteration processes corresponding to each development project; clustering each test problem in the target problem data set to obtain a corresponding key problem word set; the key problem word set is input into a preset financial requirement item character template to generate a corresponding financial requirement item, keywords for generating the financial requirement item are extracted from the divided word problem data set based on a preset financial vocabulary set, the financial requirement item generating device can also send each financial requirement item to preset display equipment for displaying, and can also send a notification message containing specific content of each financial requirement item to client equipment of developers and the like.
In another practical application, the financial requirement item generating device may perform the financial requirement item generating part in the server as described above, or all the operations may be performed in the user end device. Specifically, the selection may be performed according to the processing capability of the user end device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. If all the operations are completed in the customer premise equipment, the customer premise equipment may further include a processor for performing a specific process of generating the financial demand items.
It is understood that the mobile terminal may include any mobile device capable of loading an application, such as a smart phone, a tablet electronic device, a network set-top box, a portable computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
The mobile terminal may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
The server and the mobile terminal may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.
The following embodiments and application examples are specifically and individually described in detail.
In order to solve the problem that the existing financial demand item generation method cannot meet the requirement of financial demand item generation efficiency on the basis of ensuring the individuation of the financial demand item, the application provides an embodiment of a financial demand item generation method, and referring to fig. 2, the financial demand item generation method executed based on a financial demand item generation device specifically includes the following contents:
step 100: extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects.
In step 100, the financial vocabulary set may be pre-stored in a database local to the financial requirement item generating apparatus or accessible to the financial requirement item generating apparatus, and the financial vocabulary set is used to store the pre-set financial vocabularies such as professional vocabularies of various banking industries, and may be pre-input by the user into the database local to the financial requirement item generating apparatus or accessible to the financial requirement item generating apparatus for storage. In one or more embodiments of the present application, the test questions in the plurality of iterative processes that each development project corresponds to included in the question data set are: the problem data set is used for storing the unique identification of the test problem and the corresponding relation between the content of the test problem in a plurality of iteration processes corresponding to each development project.
Before the problem data set is subjected to word segmentation processing, the test problem content corresponding to the unique identifier of each test problem stored in the problem data set is complete test problem content; after the problem data set is subjected to word segmentation processing, the test problem content corresponding to the unique identification of each test problem stored in the problem data set is content formed by each divided word (for example, each word is separated by punctuations).
After extracting the keywords from the question data set, the test question content corresponding to the unique identifier of each test question stored in the question data set is the content composed of each financial vocabulary (i.e., the keywords), and at this time, the question data set is determined as the target question data set that needs to be clustered in step 200. That is to say, the target problem data set is still used for storing the unique identifiers of the test problems in the multiple iteration processes corresponding to each development project and the corresponding relations between the test problem contents, but the test problem contents at this time are not complete test problem contents or words after word segmentation but financial words related to the original complete test problem contents.
Step 200: and clustering each test problem in the target problem data set to obtain a corresponding key problem word set.
In step 200, clustering each test problem in the target problem data set refers to clustering each test problem in the target problem data set, which is composed of each financial vocabulary, so as to improve the accuracy of finding similar test problems in the target problem data set.
In one or more embodiments of the present application, the keyword sets are also used to store the corresponding relationship between the unique identifiers of the test questions and the content of the test questions in the multiple iterations corresponding to each development project, but the content of the test questions is the content composed of each financial vocabulary (i.e., keywords), and the number of the unique identifiers of the test questions is significantly less than that of the test questions in the target question data set, because similar test questions are clustered in step 200, so that the total number of the unique identifiers of the corresponding test questions is correspondingly reduced.
Step 300: and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item.
In step 300, a financial requirement item text template can be set, a requirement party is supported to modify manually, and a corresponding requirement item description is output by assembling a keyword set to complete output of a requirement item.
As can be seen from the above description, in the financial requirement item generation method provided in the embodiment of the present application, by extracting the keyword for generating the financial requirement item in the segmented problem data set based on the preset financial vocabulary set, the target problem data set can be specially adapted to the financial industry on the basis of effectively reducing the data amount of the segmented problem data set, and thus the reliability, accuracy and applicability of the subsequent clustering process on the target problem data set can be effectively improved; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
In order to make the target issue data set specifically suitable for the financial industry, in an embodiment of the financial requirement item generation method provided in the present application, referring to fig. 3, the step 100 of the financial requirement item generation method further includes the following steps:
step 010: and acquiring test problems in a plurality of iteration processes corresponding to each development project, and generating a corresponding problem data set.
In step 010, test problems in a plurality of iterative processes of a plurality of projects are imported, then usability problems with uncompleted modification states in the iterative processes are screened out, and a cleaned data source to be processed, namely an initial problem data set, is formed.
Step 020: the problem data set is preprocessed.
Step 030: and performing word segmentation on the preprocessed problem data set based on a preset conditional random field CRF algorithm to obtain a word-segmented problem data set.
Specifically, a CRF algorithm is used for carrying out basic Chinese word segmentation, a value set { B, E, M, S } is set for calculating the labeling probability among words, a vector F (x, y) and a weight vector w are set, an observation sequence x is set, and a recursion function is set
Figure BDA0003196080620000091
Computing, outputting the y set as the optimal path output, wherein
Figure BDA0003196080620000092
And (4) identifying professional vocabularies by combining the vocabularies of the banking industry, and screening out keywords which can be used for generating the demand items of the banking industry.
As can be seen from the above description, in the financial demand item generation method provided in the embodiment of the present application, the preprocessed problem data set is subjected to word segmentation based on the preset conditional random field CRF algorithm, so that an accurate and effective data basis can be provided for subsequently extracting the keyword for generating the financial demand item from the segmented problem data set, and further, on the basis of effectively reducing the data amount of the segmented problem data set, the target problem data set can be specially adapted to the financial industry, and further, the reliability, accuracy and applicability of subsequently clustering the target problem data set can be effectively improved.
In order to improve the effectiveness and efficiency of data preprocessing, in an embodiment of the financial requirement item generating method provided in the present application, referring to fig. 4, step 020 of the financial requirement item generating method specifically includes the following steps:
step 021: and performing data cleaning on the problem data set.
Step 022: formatting a problem data set after data cleaning so that the problem data set comprises a unique identifier of each test problem and a corresponding relation between test problem contents, wherein the test problem contents are divided by attributes, and each attribute comprises: project name, service category, service scenario, problem description and related applications.
Specifically, the cleaned data is constructed according to the attributes such as project name, service type, service scene, problem description and related application, and the data with the structured attributes is output in a regularized manner and is converted into a modeling data source capable of modeling and identifying.
As can be seen from the above description, the financial requirement item generating method provided in the embodiment of the present application formats the problem data set after data cleaning, so that the problem data set includes the corresponding relationship between the unique identifier of each test problem and the content of the test problem, and thus the effectiveness and efficiency of data preprocessing can be effectively improved, and further the efficiency and reliability of performing word segmentation on the preprocessed problem data set can be effectively improved.
In order to improve the effectiveness and the application reliability of the keywords in the target problem data set, in an embodiment of the financial requirement item generation method provided by the present application, referring to fig. 5, step 100 of the financial requirement item generation method specifically includes the following contents:
step 110: and calling a preset financial vocabulary set.
Step 120: extracting financial vocabularies in the set of financial vocabularies for generating financial demand items in the segmented problem dataset.
Step 130: and labeling the part of speech of each financial vocabulary by using a preset FudanNLP tool kit, and extracting the financial vocabulary of which the part of speech is noun and verb as a keyword to form a target problem data set consisting of each keyword.
Specifically, basic Chinese word segmentation is carried out by using a CRF algorithm, the algorithm can well solve the problems of Chinese ambiguity and the like generated in the process of describing test problems based on a sequence tagging model, professional vocabularies are identified by combining the vocabularies of the banking industry, keywords which can be used for generating the vocabularies of the banking industry are screened out, and the vocabularies are tagged by using a FudanNLP tool kit.
As can be seen from the above description, in the financial requirement item generating method provided in the embodiment of the present application, by tagging the part of speech of each financial vocabulary by using the preset FudanNLP toolkit, and extracting the financial vocabulary whose part of speech is a noun and a verb as the keyword, the validity and the application reliability of the keyword in the target problem data set can be improved on the basis of effectively reducing the data volume of the target problem data set, so that the personalization, the accuracy and the reliability of generating the financial requirement item can be further improved, and the method can be specially applied to the financial industry, and can effectively improve the automation degree and the efficiency of the financial requirement item generating process.
In order to improve the reliability and accuracy of clustering each test question in the target question data set, in an embodiment of the financial requirement item generation method provided by the present application, referring to fig. 6, the following contents are further specifically included between step 100 and step 200 of the financial requirement item generation method:
step 140: and dividing keywords corresponding to each test problem in the target problem data set into a professional noun, a non-professional noun, a professional verb and a non-professional verb based on a preset professional vocabulary division rule.
Step 150: and respectively carrying out weight assignment on the keywords belonging to the professional nouns, the non-professional nouns, the professional verbs and the non-professional verbs according to the sequence of the weighted values from large to small.
Specifically, a keyword set is constructed by keeping professional nouns, non-professional nouns, professional verbs and non-professional verbs in each question description; suppose the number of terms in the keyword set is n and a certain keyword is tiEach test question is composed of a plurality of keywords, and a question S constructed by the VSM method to construct a vector space model can be represented as S (t)1,t2,...,tn)，tiIf the problem occurs, the problem is marked as 1, and if the problem does not occur, the problem is marked as 0, so that each problem forms an n-dimensional space vector. However, if the vector calculation only adopts the calculation methods of 0 and 1, it is difficult to distinguish the similarity in detail, so the weight values w are set in turn according to the professional nouns, the non-professional nouns, the professional verbs and the non-professional verbs from large to small1To w4The default values are respectively 100, 50, 10 and 5, and a text vector S corresponding to the test question set S is constructedc＝S(w11,w21,w32,w43,......wnn)。
As can be seen from the above description, in the financial requirement item generating method provided in this embodiment of the application, by dividing the keywords corresponding to each test problem in the target problem data set into a professional noun, a non-professional noun, a professional verb, and a non-professional verb, and performing weight assignment on the keywords belonging to the professional noun, the non-professional noun, the professional verb, and the non-professional verb, respectively, an effective and reliable data basis can be provided for clustering each test problem in the target problem data set, and thus reliability and accuracy of clustering each test problem in the target problem data set can be effectively improved.
In order to improve the effectiveness of the keyword set, in an embodiment of the financial requirement item generation method provided in the present application, referring to fig. 7, a step 200 of the financial requirement item generation method specifically includes the following steps:
step 210: and clustering each test problem in the target problem data set after the keyword weight assignment based on a preset LP clustering algorithm to obtain a corresponding keyword set.
Specifically, an algorithm model is constructed by using a Layer-Partition (LP for short) clustering algorithm, the clustering algorithm inherits the idea based on division and hierarchical clustering, the distance of each cluster is calculated and the current optimal solution is found depending on the last calculation result, the similarity among all clusters is avoided being compared, the integral clustering speed is improved, and a test problem set S is set to be { S ═ S { (S })1,S2,...,SmAnd calculating similarity Sim (S) between each questioni,Sj) And the similarity calculation adopts cosine theorem calculation, and a distance threshold value is set to be alpha. Initially each SiAs a single cluster TiArbitrarily select a TiCalculating each S in turnjAnd TiIf the distance is less than alpha, then S isjIs classified as TiUntil all of the remaining SjIf the value is larger than alpha, then selecting the last T which is least similar to the clustering starting pointjAs a starting point, the calculate distance step is repeated until all clusters participate in the clustering. And setting each professional noun as a maximum keyword, and outputting a clustering result surrounding the maximum keyword.
As can be seen from the above description, the financial demand item generation method provided in the embodiment of the present application clusters each test problem in the target problem data set after assigning the keyword weight based on the preset LP clustering algorithm, so that the validity of the keyword set can be effectively improved, and the accuracy, the automation degree, and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved.
In order to improve the convenience and efficiency of learning the automatically generated financial requirement items by users such as research personnel, in an embodiment of the financial requirement item generation method provided by the present application, referring to fig. 8, the following content is further specifically included after step 300 of the financial requirement item generation method:
step 400: and outputting the financial demand item.
Specifically, each financial requirement item generated in step 300 may be sent to a preset display device for displaying, or an announcement message including specific content of each financial requirement item may be sent to a client device of a developer or the like.
As can be seen from the above description, according to the financial demand item generation method provided in the embodiment of the application, by outputting the financial demand item, convenience and efficiency of users such as research and development personnel who know the automatically generated financial demand item can be effectively improved, user experience of the research and development personnel can be further improved, efficiency and reliability of getting online and optimizing financial software products according to the financial demand item can be further improved, and user experience of developers of financial software products can be effectively improved.
In terms of software, in order to solve the problem that the existing financial requirement item generating method cannot meet the requirement of financial requirement item generating efficiency on the basis of ensuring the personalization of the financial requirement item, the present application provides an embodiment of a financial requirement item generating device for executing all or part of the content in the financial requirement item generating method, and referring to fig. 9, the financial requirement item generating device specifically includes the following contents:
a data extraction module 10, configured to extract keywords used for generating financial requirement items in a problem data set of divided words based on a preset financial vocabulary set, so as to obtain a target problem data set composed of the keywords, where the problem data set includes test problems in multiple iteration processes corresponding to each development project;
a data clustering module 20, configured to cluster each test problem in the target problem data set to obtain a corresponding keyword set;
the template generating module 30 is configured to input the keyword set into a preset financial requirement item text template to generate a corresponding financial requirement item.
The embodiment of the financial requirement item generating apparatus provided in the present application may be specifically configured to execute the processing flow of the embodiment of the financial requirement item generating method in the foregoing embodiment, and the functions of the processing flow are not described herein again, and reference may be made to the detailed description of the above method embodiment.
As can be seen from the above description, the financial requirement item generating device provided in the embodiment of the present application extracts the keyword for generating the financial requirement item in the segmented problem data set based on the preset financial vocabulary set, so that the target problem data set can be specially adapted to the financial industry on the basis of effectively reducing the data amount of the segmented problem data set, and further, the reliability, accuracy and applicability of the subsequent clustering process on the target problem data set can be effectively improved; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
In order to further explain the scheme, the application example provides a financial demand item generation method based on a test problem set and an LP clustering algorithm, relates to the field of product requirements, and aims to solve the problem that in the field of product requirements, in the process of outputting demand items, no method for automatically generating demand items without manpower is needed.
The method for automatically generating the demand items based on the test question set provided by the application example of the application mainly comprises the following steps:
step 1): and the data preprocessing device is used for preprocessing the test problem set, resolving the test problem set into a standardized problem format containing project names, service types, service scenes, problem description, application related and the like, and providing the new data set to the cluster analysis device.
Step 2): the device comprises a clustering analysis device, a word segmentation algorithm of a conditional random field CRF algorithm is used as a basic word segmentation algorithm, a special test question set word segmentation tool is formed by combining professional vocabularies in the banking industry, and the part of speech is labeled by using a FudanNLP tool bag. Compared with the traditional word segmentation mode, the method can identify the bank professional nouns, provides a part-of-speech tagging function, and can conveniently organize the keyword word sequence finally generated by the demand items through part-of-speech tagging. The clustering analysis model is constructed by using the LP clustering algorithm to find the keyword set of the similar test problem scene, the clustering algorithm inherits the idea based on division and hierarchical clustering, the similarity between all clusters is avoided being compared, the overall clustering speed can be improved, and keywords can be conveniently provided for the demand item generating device.
Step 3): and the requirement item generating device is provided with a special requirement item template, supports a demand side to carry out manual modification, and outputs corresponding requirement item descriptions through assembling the keyword set to complete output of the requirement items.
Referring to fig. 10, a financial requirement item generating system for implementing a financial requirement item generating method provided by an application example of the present application specifically includes the following contents:
the device comprises a data preprocessing device, a cluster analysis device and a demand item generating device.
Wherein, the data preprocessing device is connected with the cluster analysis device; the cluster analysis device is connected with the demand item generation device.
(1) A data preprocessing device: the method is used for cleaning and processing an original test problem data set, comprises test problems in a plurality of iteration processes of a plurality of projects, screens out usability problems, and cleans and constructs attributes of data, wherein the attributes comprise project names, service types, service scenes, problem description, related applications and the like. And after reconstruction is finished, providing the data to a clustering analysis device.
(2) A cluster analysis device: and the method is used for inputting the preprocessed data source, adopting a clustering algorithm to construct a model, and outputting a keyword set corresponding to various service scenes. The CRF algorithm is firstly utilized to carry out basic Chinese word segmentation, the algorithm is based on a sequence tagging model, the problems of Chinese ambiguity and the like generated when a test problem is described can be well processed, professional vocabularies are identified by combining the vocabularies of the banking industry, keywords which can be used for generating the vocabularies of the banking industry are screened out, and the vocabularies are tagged by utilizing a FudanNLP tool bag. On the basis of carrying out Chinese word segmentation and labeling parts of speech such as nouns and verbs, the word segmentation weights of professional vocabularies in the banking industry and the nouns are required to be increased, a clustering analysis model is built by using an LP clustering algorithm to find a key problem word set of a similar test problem scene, and the key problem word set is provided for a demand item generating device.
(3) The requirement item generating device: the method is used for setting a demand item character template to automatically fill in the keyword set to form a complete demand item by taking the keyword set obtained by clustering analysis as an input data source, supporting manual modification and storage, and finally outputting the complete demand item generated based on the test question set.
Referring to fig. 11, the data preprocessing apparatus 1 specifically includes the following contents:
a data acquisition unit 11, a data cleansing unit 12, an attribute construction unit 13, and a data transformation unit 14.
(1) The data acquisition unit 11: a test problem in a plurality of iterative processes for importing a plurality of items.
(2) The data cleansing unit 12: and screening out the usability problem that the state is not finished and modified in the iterative process, and forming a cleaned data source to be processed.
(3) The attribute construction unit 13: the method is used for constructing the cleaned data according to the project name, the service category, the service scene, the problem description, the related application and other attributes.
(4) The data conversion unit 14: the modeling data source is used for regularly outputting the data after the attribute construction and transforming the data into a modeling data source which can be modeled and identified.
See table 1 for an example of the test problem:
TABLE 1 test problem example table
Figure BDA0003196080620000151
Referring to fig. 12, the cluster analysis apparatus 2 specifically includes the following contents:
a word segmentation unit 21, a data filtering unit 22, a weight calculation unit 23, a cluster calculation unit 24, and a result output unit 25.
(1) Word segmentation unit 21: utilizing CRF algorithm to carry out basic Chinese word segmentation, setting a value set { B, E, M, S } for calculating the labeling probability between words, setting a vector F (y, x), a weight vector w, an observation sequence x and a recursion function
Figure BDA0003196080620000152
Computing, outputting the y set as the optimal path output, wherein
Figure BDA0003196080620000161
And identifying professional vocabularies by combining the vocabularies of the banking industry, screening out keywords which can be used for generating requirement items of the banking industry, and labeling the parts of speech by utilizing a FudanNLP toolkit. On the basis of carrying out Chinese word segmentation and labeling the parts of speech such as nouns and verbs, useless words such as special symbols, adjectives and auxiliary words need to be filtered, so that the dimension reduction of a word vector model is facilitated, and the keyword word sequence generated finally by a required item can be organized conveniently through part of speech labeling.
(2) The data filtering unit 22: and reserving nouns belonging to the professional class, nouns belonging to the non-professional class, verbs belonging to the professional class and verbs belonging to the non-professional class in each question description to construct a keyword set.
(3) Weight calculation unit 23: suppose the number of terms in the keyword set is n and a certain keyword is tiEach test question is composed of a plurality of keywords, and a question S constructed by the VSM method to construct a vector space model can be represented as S (t)1,t2,...,tn)，tiIf the problem occurs, the problem is marked as 1, and if the problem does not occur, the problem is marked as 0, so that each problem forms an n-dimensional space vector. However, if the vector calculation only adopts the calculation methods of 0 and 1, it is difficult to distinguish the similarity in detail, so the weight values w are set in turn according to the professional nouns, the non-professional nouns, the professional verbs and the non-professional verbs from large to small1To w4The default values are respectively 100, 50, 10 and 5, and a text vector S corresponding to the test question set S is constructedc＝S(w11,w21,w32,w43,......wnn)。
(4) The cluster calculating unit 24: an algorithm model is constructed by using a Layer-Partition (LP for short) clustering algorithm, the clustering algorithm inherits the idea based on division and hierarchical clustering, the cluster distance is calculated each time, the current optimal solution is found depending on the last calculation result, the similarity among all clusters is avoided being compared, the integral clustering speed is improved, and a test problem set S is set to be { S ═ S1,S2,...,SmAnd calculating similarity Sim (S) between each questioni,Sj) And the similarity calculation adopts cosine theorem calculation, and a distance threshold value is set to be alpha. Initially each SiAs a single cluster TiArbitrarily select a TiCalculating each S in turnjAnd TiIf the distance is less than alpha, then S isjIs classified as TiUntil all of the remaining SjIf the value is larger than alpha, then selecting the last T which is least similar to the clustering starting pointjAs a starting point, the calculate distance step is repeated until all clusters participate in the clustering. And setting each professional noun as a maximum keyword, and outputting a clustering result surrounding the maximum keyword.
(5) The result output unit 25: and the clustering module is used for sending the clustering result to the character template.
Referring to fig. 13, the requirement item generating device 3 specifically includes the following contents:
a text template setting unit 31, a text typesetting unit 32, a personalization setting unit 33 and a requirement item publishing unit 34.
(1) The text template setting unit 31: presetting a character template generated by a plurality of demand items, and filling and outputting according to a reserved template when the demand items are issued;
(2) text composition unit 32: typesetting the generated demand items, beginning with two empty grids, and finally adding empty row symbols to perform a simple beautifying function;
(3) the personalization setting unit 33: the functions of manually inputting a new template, modifying an existing character template, deleting the template and the like are supported, and modification and storage are supported after the system automatically generates a demand item.
(4) The demand item issuing unit 34: and generating a complete demand item set according to the keywords. Table 1 and table 2 show the comparison result of the original question and the final generated requirement item, after clustering, the template is used to 'channel class', 'noun', initiate 'scene class', 'noun' needs to support 'verb'. "auto fill generates required items.
See table 2 for examples of output requirements:
table 2 output requirement item example table
Figure BDA0003196080620000171
The financial demand item generation method provided by the application example of the application example is based on a test problem set generated in a project iteration process, a clustering model based on a natural language processing technology is built, keywords in a service scene and a problem description are found, and the product demand of the next iteration project is automatically induced and generated. Its advantages are as follows:
based on the original data of the test problem set, the discovered product defects can be collected again by saving manpower, the use value of the usability problem in the test problem is improved, and more functions are played in the whole period of the project.
Keywords in the problems can be found in a self-learning mode through a clustering algorithm, related scenes of the keywords are clustered and mined, and time cost for collecting the problems and summarizing the same kind of problems through manpower is greatly saved.
The requirement item depending on the text template is automatically generated, so that the time for a demand side to write the requirement item can be saved, and the template can be generated by using a personalized tool, so that the diversity generated by different requirements is kept.
In terms of hardware, in order to solve the problem that the existing financial requirement item generation method cannot meet the requirement of financial requirement item generation efficiency on the basis of ensuring the personalization of the financial requirement item, the present application provides an embodiment of an electronic device for implementing all or part of the content in the financial requirement item generation method, where the electronic device specifically includes the following content:
fig. 14 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 14, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 14 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the financial demand item generation functionality may be integrated into the central processor. Wherein the central processor may be configured to control:
step 100: extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects.
Step 200: and clustering each test problem in the target problem data set to obtain a corresponding key problem word set.
Step 300: and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item.
As can be seen from the above description, according to the electronic device provided in the embodiment of the present application, the keyword for generating the financial requirement item is extracted from the problem data set of the divided words based on the preset financial vocabulary set, so that the target problem data set can be specially adapted to the financial industry on the basis of effectively reducing the data amount of the problem data set of the divided words, and the reliability, accuracy and applicability of the subsequent clustering process on the target problem data set can be effectively improved; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
In another embodiment, the financial requirement item generating device may be configured separately from the central processor 9100, for example, the financial requirement item generating device may be configured as a chip connected to the central processor 9100, and the financial requirement item generating function is realized by the control of the central processor.
As shown in fig. 14, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 14; further, the electronic device 9600 may further include components not shown in fig. 14, which can be referred to in the related art.
As shown in fig. 14, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
Embodiments of the present application further provide a computer-readable storage medium capable of implementing all the steps in the financial requirement item generating method in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all the steps of the financial requirement item generating method in which an execution subject is a server or a client in the above embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
step 100: extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects.
Step 200: and clustering each test problem in the target problem data set to obtain a corresponding key problem word set.
Step 300: and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application, by extracting the keyword for generating the financial requirement item in the problem data set of the divided words based on the preset financial vocabulary set, can enable the target problem data set to be specially adapted to the financial industry on the basis of effectively reducing the data amount of the problem data set of the divided words, and can further effectively improve the reliability, accuracy and applicability of the subsequent clustering process on the target problem data set; clustering is carried out on each test problem in the target problem data set to obtain a corresponding key problem word set, so that the accuracy, the automation degree and the intelligent degree of finding similar test problems in the target problem data set can be effectively improved; by inputting the keyword sets into the preset financial demand item character templates, the individuation, the accuracy and the reliability of the generated financial demand items can be effectively improved, the automation degree and the efficiency of the financial demand item generation process can be effectively improved, the efficiency and the reliability of online and optimized financial software products according to the financial demand items are further improved, and the user experience of financial software product developers is effectively improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A financial demand item generation method, comprising:
extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set to obtain a target problem data set consisting of the keywords, wherein the problem data set comprises test problems in a plurality of iteration processes corresponding to development projects;
clustering each test problem in the target problem data set to obtain a corresponding key problem word set;
and inputting the keyword set into a preset financial demand item text template to generate a corresponding financial demand item.
2. The method as claimed in claim 1, further comprising, before extracting keywords for generating the financial demand item in the segmented question data set based on the preset financial vocabulary set, the steps of:
obtaining test problems in a plurality of iteration processes corresponding to each development project, and generating a corresponding problem data set;
preprocessing the problem data set;
and performing word segmentation on the preprocessed problem data set based on a preset conditional random field CRF algorithm to obtain a word-segmented problem data set.
3. The financial demand item generation method of claim 2, wherein the pre-processing the issue data set comprises:
performing data cleaning on the problem data set;
formatting a problem data set after data cleaning so that the problem data set comprises a unique identifier of each test problem and a corresponding relation between test problem contents, wherein the test problem contents are divided by attributes, and each attribute comprises: project name, service category, service scenario, problem description and related applications.
4. The method as claimed in claim 1, wherein the extracting of the keyword for generating the financial requirement item in the problem data set of divided words based on the preset financial vocabulary set comprises:
calling a preset financial vocabulary set;
extracting financial vocabularies used for generating financial demand items in the financial vocabulary set from the divided word problem data set;
and labeling the part of speech of each financial vocabulary by using a preset FudanNLP tool kit, and extracting the financial vocabulary of which the part of speech is noun and verb as a keyword to form a target problem data set consisting of each keyword.
5. The method of claim 4, wherein before clustering each test question in the target question data set to obtain a corresponding set of keyword terms, further comprising:
dividing keywords corresponding to each test problem in the target problem data set into a professional noun, a non-professional noun, a professional verb and a non-professional verb based on a preset professional vocabulary division rule;
and respectively carrying out weight assignment on the keywords belonging to the professional nouns, the non-professional nouns, the professional verbs and the non-professional verbs according to the sequence of the weighted values from large to small.
6. The method of claim 5, wherein the clustering each test question in the target question data set to obtain a corresponding set of keyword terms further comprises:
and clustering each test problem in the target problem data set after the keyword weight assignment based on a preset LP clustering algorithm to obtain a corresponding keyword set.
7. The financial demand item generation method according to any one of claims 1 to 6, further comprising:
and outputting the financial demand item.
8. A financial demand item generating apparatus, comprising:
the system comprises a data extraction module, a data analysis module and a data analysis module, wherein the data extraction module is used for extracting keywords for generating financial demand items in a problem data set of divided words based on a preset financial vocabulary set so as to obtain a target problem data set consisting of the keywords, and the problem data set comprises a plurality of test problems in a plurality of iteration processes corresponding to development projects; the data clustering module is used for clustering each test problem in the target problem data set to obtain a corresponding keyword set; and the template generating module is used for inputting the keyword set into a preset financial demand item text template so as to generate a corresponding financial demand item.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of generating a financial demand item of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the financial demand item generation method of any one of claims 1 to 7.
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