CN113159738A - Business item processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the specification relates to the technical field of big data, can be used in the field of financial science and technology or other fields, and particularly discloses a business project processing method, a business project processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving a target requirement item of a business project; the target requirement item is used for representing a sub-development link in the business project development process; extracting service key information of the target demand item, and determining a service type to which the target demand item belongs based on the service key information to serve as a target service type; obtaining a product application association rule corresponding to a target service type, wherein the product application association rule is used for representing a potential association relation between product applications related to the target service type; and determining the product application associated with the target requirement item based on the product application association rule so as to realize the processing of the target requirement item by utilizing the determined product application, thereby greatly improving the efficiency of business project development.
Description
Technical Field
The present disclosure relates to the field of big data technology, and may be used in the field of financial technology or other fields, and in particular, to a method and an apparatus for processing a business project, an electronic device, and a storage medium.
Background
In the internet era, in order to quickly adapt to business requirements, the development process of a project is gradually converted from a traditional mode to agile development so as to adapt to the market requirement of quick online products. Each project is generally composed of a plurality of requirement items, and each requirement item is jointly developed and realized by one or more product applications.
At present, in the process of project development, after a demand side puts forward demands, a task list can be issued through a system management platform. The architects and other experienced employees can split the requirement items from top to bottom based on the architectural design and work experience for the project requirement task sheet to distribute the functional implementation of the requirement items to one or more product applications. However, this allocation method is too dependent on human experience, and is prone to human error when the workload is too high. With the increasing development of technology, demand items of projects are increasing, and application allocation of the demand items becomes a matter of severe consumption of labor cost.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method and an apparatus for processing a business project, an electronic device, and a storage medium, which can greatly improve the efficiency of business system development.
The present specification provides a method, an apparatus, an electronic device and a storage medium for processing a business project, which are implemented in the following ways:
a business project processing method is applied to a server, and the method comprises the following steps: receiving a target requirement item of a business project; the target requirement item is used for representing a sub-development link in the business project development process; extracting service key information of the target demand item, and determining a service type to which the target demand item belongs based on the service key information to serve as a target service type; obtaining a product application association rule corresponding to the target service type, wherein the product application association rule is used for representing a potential association relation among product applications related to the target service type; and determining the product application associated with the target demand item based on the product application association rule so as to realize the processing of the target demand item by utilizing the determined product application.
In still other embodiments provided by the method described in this specification, the determining the service type to which the target demand item belongs based on the service key information includes: and processing the service key information by utilizing a pre-constructed service classification model to obtain the service type of the target demand item.
In other embodiments provided by the method described in this specification, the service classification model is constructed in the following manner: extracting a requirement item, service key information of the requirement item and a service type of the requirement item from project data of a developed service project; for any demand item, constructing an input vector of the corresponding demand item by using the business key information of the demand item, and taking the business type of the demand item as a label of the corresponding demand item; taking the input vector and the label of the demand item as a sample to obtain a sample set; and constructing a business classification model by using the sample set.
In other embodiments provided by the method described in this specification, the sample set is processed using a naive bayes classification algorithm to obtain a service classification model.
In still other embodiments provided by the methods described herein, the method further comprises: extracting requirement items, service types to which the requirement items belong and product applications for realizing the corresponding requirement items from the project data of the developed service projects, and taking the requirement items, the service types to which the requirement items belong and the product applications as data sources; extracting product applications related to any service type from the data source to obtain a product application set corresponding to the corresponding service type; and for any service type, extracting the potential association relation among product applications in a product application set corresponding to the service type to obtain a product application association rule of the corresponding service type.
In other embodiments provided by the method described in this specification, an Apriori algorithm is used to extract a potential association relationship between product applications in a product application set corresponding to the service type.
In other embodiments provided by the method described in this specification, the extracting, by using Apriori algorithm, the potential association relationship between product applications in the product application set corresponding to the service type includes: extracting a frequent item set of the product application set under the k item set; wherein the k item set refers to a set containing k product applications in the product application set; the initial value of k is 1, k is a positive integer less than or equal to n-1, and n is the total product application number of the product application set; constructing a k +1 item set of the product application set based on the frequent item set under the k item set, and extracting the frequent item set of the k +1 item set; executing the iteration steps until the value of k is equal to n-1, and outputting a frequent item set of the product application set under each item number; screening out a frequent item set with the confidence coefficient larger than the confidence coefficient parameter from the frequent item sets under the terms; and determining potential association relations among the product applications in the product application set based on the screened frequent item sets.
In another aspect, an embodiment of the present specification further provides a business item processing apparatus, where the apparatus includes: the receiving module is used for receiving a target demand item of a business project; the target requirement item is used for representing a sub-development link in the business project development process; the extraction module is used for extracting the business key information of the target demand item, determining the business type of the target demand item based on the business key information and taking the business type as a target business type; the association rule obtaining module is used for obtaining a product application association rule corresponding to the target service type, and the product application association rule is used for representing a potential association relation among product applications related to the target service type; and the association application determination module is used for determining the product application associated with the target requirement item based on the product application association rule so as to realize the processing of the target requirement item by utilizing the determined product application.
In other embodiments provided by the apparatus described herein, the apparatus further comprises: the data source extraction module is used for extracting a requirement item, a service type to which the requirement item belongs and product application for realizing the corresponding requirement item from project data of a developed service project as a data source; and the application set extraction module is used for extracting the product application related to any service type from the data source to obtain a product application set association rule extraction module corresponding to the corresponding service type, and is used for extracting the potential association relationship among the product applications in the product application set corresponding to the service type to obtain the product application association rule corresponding to the service type.
In another aspect, an embodiment of the present specification further provides an electronic device, where the electronic device includes at least one processor and a memory for storing processor-executable instructions, and the instructions, when executed by the processor, implement the steps of the method according to any one or more of the above embodiments.
In another aspect, the present specification further provides a computer readable storage medium, on which computer instructions are stored, and the instructions, when executed, implement the steps of the method according to any one or more of the above embodiments.
In the service project processing method, the service project processing device, the electronic device, and the storage medium provided in one or more embodiments of the present specification, by extracting product applications that may be related to a specific service type in advance, and extracting potential association relationships between product applications related to the specific service type, in an actual project development process, a server may analyze a target project requirement to determine a target service type related to the target project requirement, and then determine a product application related to the target project requirement based on the potential association relationships between product applications related to the target service type, so that efficiency and accuracy of determining the product application related to the project requirement may be greatly improved, manpower screening cost is reduced, and efficiency of developing the service project is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification 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, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
FIG. 1 is a block diagram of a business item processing device in one embodiment provided in this specification;
FIG. 2 is a block diagram of a business item processing apparatus in an embodiment provided in the present specification;
FIG. 3 is a block diagram of a business item processing system in one embodiment provided by the present specification;
FIG. 4 is a block diagram of a data preprocessing apparatus in one embodiment provided in the present specification;
FIG. 5 is a block diagram of an associated mining device in one embodiment provided herein;
fig. 6 is a schematic block diagram of a division generating apparatus in an embodiment provided in the present specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on one or more embodiments of the present specification without making any creative effort shall fall within the protection scope of the embodiments of the present specification.
Fig. 1 is a flowchart illustrating an embodiment of a business item processing method provided in this specification. As shown in fig. 1, an embodiment of the present specification further provides a business item processing method, which may be applied to a server. Accordingly, the method may comprise the following steps.
S102: receiving a target requirement item of a business project; and the target requirement item is used for representing a sub-development link in the business project development process.
The business project can refer to the processing processes of business system development, maintenance, perfection and the like. The enterprise can set the processing processes of business system development, maintenance, perfection and the like as a series of system development projects according to the needs of the enterprise, so that the resource allocation and management are facilitated. The processing content contained in any service item can be set according to the requirement.
The business items can be decomposed in advance according to needs. For example, the processing procedure of the service project can be split according to the functions, data processing logic and the like required to be realized by the service project, so as to obtain a series of project units. The item unit may be described as a requirement item. Correspondingly, the requirement item can refer to any sub-development link in the development process of the business project.
The functions, data processing logic and the like required to be realized by the requirement items can be configured according to needs, for example, in the process of developing the service project, each requiring party can newly establish the requirement items on a system management platform for developing the service project, and can also configure requirement description information of the requirement items. The requirement description information may include, for example, service information for the target, functions to be implemented, standards to be achieved, and the like. After completion of filling, the demander can issue a task allocation instruction corresponding to the demand item through the system management platform. Such that the server determines a product application for implementing the requirements item based on the instructions. The requirement item to be task-allocated may be taken as a target requirement item.
S104: and extracting the service key information of the target demand item, and determining the service type of the target demand item based on the service key information to serve as the target service type.
The server can acquire the requirement description information of the target requirement item from a task allocation instruction of a system management platform and extract the service key information of the requirement description information. The requirement description information may include various types of information, such as service information for which the requirement is addressed, functions required to be implemented, standards required to be achieved, and the like, and there is a lot of noise information, and it is difficult to accurately determine the service type to which the target requirement item belongs by directly using the requirement description information. In some embodiments, the requirement description information may be processed in advance to extract a keyword and the like capable of representing a service type as service key information, and then the service type to which the target requirement item belongs is determined based on the service key information, so that accuracy of determining the service type may be greatly improved.
For example, the CRF algorithm may be used to perform word segmentation on the requirement description information, and a value set { B, E, M, S } is set for calculating the labeling probability between the words, where the value set { B, E, M, S } is used to represent the lexeme information of the words, B is the beginning-of-word position, M is the middle-of-word position, E is the end-of-word information, and S is a single-word. Setting a vector F (y, x), a weight vector w, an observation sequence x and a recurrence function
Computing, outputting the y set as the optimal path output, wherein
And the part-of-speech is labeled by utilizing a FudanNLP toolkit, the parts-of-speech such as nouns and verbs are labeled, useless words such as special symbols, adjectives and auxiliary words are filtered, and the dimension reduction of a word vector model is facilitated. By means of the method, the initial keywords in the requirement description information can be extracted. And then, professional vocabularies can be further identified by combining with banking vocabularies, keywords for representing service information are screened from the initial keywords to form service key information, and the service type of the target demand item is determined based on the service key information and is used as the target service type.
In some embodiments, the service key information may be processed by using a service classification model constructed in advance, so as to obtain a service type to which the target requirement item belongs. The corresponding business type of the target demand item is determined in a mode of pre-constructing a classification model, so that the accuracy and the efficiency of determining the business type can be greatly improved.
For example, project data of a developed project may be obtained, and the project data may include requirement item information, requirement description information related to the requirement item, a business type to which the requirement item belongs, a product application associated with the requirement item, and the like. The project data of the developed project comprises series of information configured by business personnel in the historical project development process, and the classification model is constructed based on the information, so that the accuracy and stability of the classification model determination can be further improved. The service key information extraction method can be used for processing the requirement description information of each requirement item to obtain the service key information corresponding to the corresponding requirement item. The business key information of the demand items can be used as input information, and the business type of the demand items is used as a label to construct a sample. The samples can then be used to build a traffic classification model.
The traffic classification model is constructed, for example, by training the samples using a naive bayes classification algorithm. For example, an input vector may be constructed with the service keyword corresponding to the requirement item, that is, the service key information may be represented by using the keyword vector. Correspondingly, the business key information corresponding to the requirement item may be denoted as t ═ t1,t2,...,tnThe service type is denoted as ci。
For any input vector t, it can be calculated that the current vector t is determined as the class ciConditional probability p (c) ofiI t), wherein the highest probability can be determined as belonging to class ci. Relying on Bayesian criteria can result in
By unfolding the vector t into independent features, p (t | c)i)＝p(t1|ci)p(t2|ci)p(t3|ci)...p(tn|ci) I.e. each term is in category ciProbability product of occurrence in (c). In addition, in the actual calculation process, the probability product is multiplied by a plurality of very small numbers, so that calculation overflow is easily caused, therefore, the product calculation is optimized by adopting an ln logarithm taking mode, and finally p (c) is obtainediThe value of | t). If p (c)1|t)>p(c2I t), the category corresponding to the t vector is considered to belong to c1If p (c)1|t)<p(c2I t), the category corresponding to the t vector is considered to belong to c2。
The input vector of the sample can be processed by using the algorithm, and the parameters in the algorithm are adjusted by using the label, so that the trained service classification model is obtained. And (3) taking a part of samples as test data, and carrying out iterative training to calculate the average error rate until the error rate reaches an acceptable range to be used as a final business classification model.
Accordingly, in some embodiments, the service classification model may be constructed in the following manner: extracting a requirement item, service key information of the requirement item and a service type of the requirement item from project data of a developed service project; for any requirement item, constructing an input vector of the corresponding requirement item by using the business key information of the requirement item, and taking the business type of the requirement item as a label of the corresponding requirement item; taking the input vector and the label of the demand item as a sample to obtain a sample set; and constructing a business classification model by using the sample set. Preferably, the sample set can be processed by using a naive Bayes classification algorithm to obtain a service classification model.
The system management platform can display the service type of the target requirement item determined by the server, and service personnel can adjust the service type of the target requirement item, such as adding, modifying, deleting and the like. The classification model is constructed based on historical data, certain hysteresis possibly exists in actual service scene change, and the service type of the target requirement item can be more accurate by supporting manual adjustment of service personnel, so that the accuracy of subsequent service application association is improved.
S106: and acquiring a product application association rule corresponding to the target service type, wherein the product application association rule is used for representing a potential association relation between product applications related to the target service type.
The server can extract the requirement item, the service type of the requirement item and the product application for realizing the corresponding requirement item from the project data of the developed service project in advance as a data source. The product application may refer to a program set for implementing a certain application function. The product application may be integrated in an APP of a service terminal or a user terminal, or may refer to functional software that implements a certain function in a service system, or may be a program set for implementing a certain specific function in other forms.
The product applications related to any service type can be extracted from the data source to obtain a product application set corresponding to the service type, so that the potential association relationship among the product applications in the product application set corresponding to the service type is extracted to obtain the product application association rule of the corresponding service type. Of course, the system management platform may also show the product applications related to each service type to the service personnel, and support the service personnel to adjust the product applications related to each service type, such as adding, modifying, and deleting product applications in a product application set of each service type. Table 1 is an example of the product application to which the service type relates.
TABLE 1
| Type of service | Product application |
| A | {F-a,F-c,F-d} |
| B | {F-b,F-c,F-e} |
| C | {F-a,F-b,F-c,F-e} |
| D | {F-b,F-e} |
Corresponding to any service type, the server can also extract the potential association relationship among product applications in a product application set corresponding to the service type by using an Apriori algorithm in advance, and the potential association relationship is used as a product application association rule corresponding to the corresponding service type.
For any product application set, one product application can be used as one item, and the item set of the current product application set is constructed. If a set of items contains k product applications, the set of items may be described as a set of k items.
The support degree parameter N and the confidence degree parameter M may be preset, and the preset support degree parameter N and the preset confidence degree parameter M are used to extract a potential association relationship between product applications in the product application set. Wherein, the probability of simultaneous occurrence of items in the item set is called the support degree of the item set. The probability that a term or a sub-term set also occurs in the case that a term or a sub-term set occurs in the term set is referred to as the confidence of the term set. If the support degree of the item set is greater than N, the item set can be considered as a frequent item set. After the frequent item set is extracted, if the confidence of a certain frequent item set is greater than M, it can be considered that each product application in the frequent item set has a strong association relationship, and a potential association relationship between each product application in the product application set can be determined based on the frequent item set.
A term set can be considered to be a non-frequent term set in general, and all its supersets are also infrequent, so a k term set with a k value of 1 can be constructed first. Traversing each product application in the product application set to obtain n k item sets, wherein n is the total product application number of the product application set. The k item set with the support degree greater than or equal to the support degree parameter n can be reserved as a frequent item set under the k item set. Then, a k +1 item set (namely, an item set containing two product applications) is constructed by taking a frequent item set under the k item set as a reference; and then extracting a frequent item set under the k +1 item set based on the support degree. Then, taking the item set containing two product applications as a k item set, and taking a frequent item set under the k item set as a reference, and further constructing a k +1 item set (namely constructing an item set containing three product applications); and then extracting a frequent item set under the k +1 item set based on the support degree. And repeating the steps until a frequent item set containing n product applications is extracted. Table 2 is an example table of the frequent item set for product application, which shows the frequent item set generation results with support degrees of 0.5 and 0.7, respectively.
TABLE 2
After obtaining the frequent itemsets, the server may further calculate the confidence level for each of the frequent itemsets. And outputting the frequent item set with the confidence coefficient larger than M. The product applications in the frequent item set screened by the method have strong association relationship, namely when one product application in the screened frequent item set appears, the probability that other product applications in the frequent item set appear simultaneously is high. The potential association relationship of each application product in the product application set can be determined based on the screened frequent item set, and the potential association relationship is used as a product application association rule of a corresponding service type.
Table 3 is an example table of the product application association rule of a certain service type, and shows the rule generation results with confidence degrees of 0.7 and 0.6, respectively. For example, { F-a } - > { F-c } indicates that { F-c } has a higher probability of occurring when { F-a } product application occurs. That is, if the requirement item is associated with the product application { F-a }, and the product application association rule of the service type related to the requirement item includes { F-a } - > { F-c }, the product application { F-c } can be used as a recommended product application, so that the product application possibly related to the requirement item can be determined quickly and accurately.
TABLE 3
The system management platform can also display the constructed product application association rule to business personnel and support the business personnel to adjust the product application association rule.
Based on the solutions provided by the above embodiments, in some embodiments, the server may extract, from project data of a developed service project, a requirement item, a service type to which the requirement item belongs, and a product application for implementing a corresponding requirement item, as a data source; extracting product applications related to any service type from the data source to obtain a product application set corresponding to the corresponding service type; and for any service type, extracting the potential association relation among product applications in a product application set corresponding to the service type to obtain a product application association rule of the corresponding service type.
In other embodiments, the server may extract a potential association relationship between product applications in a product application set corresponding to the service type by using an Apriori algorithm.
In other embodiments, the server extracts a frequent itemset of the product application set under the k itemset; wherein the k item set refers to a set containing k product applications in the product application set; the initial value of k is 1, k is a positive integer less than or equal to n-1, and n is the total product application number of the product application set; constructing a k +1 item set of the product application set based on the frequent item set under the k item set, and extracting the frequent item set of the k +1 item set; and executing the iteration steps until the value of k is equal to n-1, and outputting a frequent item set of the product application set under each item number. Then, a frequent item set with the confidence coefficient larger than the confidence coefficient parameter can be screened out from the frequent item sets under the various numbers; and determining potential association relations among the product applications in the product application set based on the screened frequent item sets.
By means of the method, the product application with the strong association relation in the same service scene can be determined more accurately, and then when the product application corresponding to the demand item is determined, the product application possibly related to the demand item can be determined quickly and accurately based on the association relation, so that the accuracy and the efficiency of product application screening are improved.
S108: and determining the product application associated with the target demand item based on the product application association rule so as to realize the processing of the target demand item by utilizing the determined product application.
The system management platform can also support business personnel to configure the product application which is possibly related to a greater extent under each target business type corresponding to the target demand item as the specified product application. The server can call product application association rules corresponding to the target service types, and then screen out product applications which are associated with the specified product applications based on the product application association rules to serve as recommended product applications, so that the target demand items are realized by the recommended product applications and the specified product applications. Table 4 illustrates an example of product applications to which the target demand items are associated. Wherein, the product application association rule based on the results of table 4 is extracted by using support degree 0.5 and confidence degree 0.7. The system management platform can also display the results in table 4, and business personnel can also adjust the results and confirm the results on the system management platform.
After the business application associated with the requirement item is determined, the realization of the requirement item can be distributed to each business application, so that each function or processing logic requirement of the requirement item is completed based on each business application, and the efficiency of business project development is improved.
TABLE 4
According to the scheme provided by the embodiment, the product applications possibly related under the specified service type are extracted in advance, the potential association relationship among the product applications related under the specified service type is extracted, in the actual project development process, the server can analyze the target project requirement to determine the target service type related to the target project requirement, and further determine the product application related to the target project requirement based on the potential association relationship among the product applications under the target service type, so that the efficiency and the accuracy of determining the product application related to the project requirement can be greatly improved, the manpower screening cost is reduced, and the efficiency of developing the service project is greatly improved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. For details, reference may be made to the description of the related embodiments of the related processing, and details are not repeated herein.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the method provided by the foregoing embodiment, this specification further provides a business item processing apparatus, as shown in fig. 2, the apparatus may include:
a receiving module 202, configured to receive a target requirement item of a business project; and the target requirement item is used for representing a sub-development link in the business project development process.
The extracting module 204 may be configured to extract the service key information of the target demand item, so as to determine, based on the service key information, a service type to which the target demand item belongs, as a target service type.
The association rule obtaining module 206 may be configured to obtain a product application association rule corresponding to the target service type, where the product application association rule is used to characterize a potential association relationship between product applications related to the target service type.
The associated application determining module 208 may be configured to determine, based on the product application association rule, a product application associated with the target demand item, so as to implement the processing of the target demand item by using the determined product application.
In other embodiments, the apparatus further comprises:
and the data source extraction module is used for extracting the requirement items, the service types to which the requirement items belong and product applications for realizing the corresponding requirement items from the project data of the developed service projects as data sources.
The application set extraction module is used for extracting the product application related to any service type from the data source to obtain a product application set corresponding to the corresponding service type;
and the association rule extraction module is used for extracting the potential association relation among the product applications in the product application set corresponding to any service type to obtain the product application association rule of the corresponding service type.
It should be noted that the above-mentioned apparatus may also include other embodiments according to the description of the above-mentioned embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The present specification also provides a scenario example of a business project processing system. Fig. 3 is a block diagram of the system. As shown in fig. 3, the system may include a data preprocessing device, an association mining device, and a division work generation device. The data preprocessing device is connected with the associated mining device, and the associated mining device is connected with the division generating device.
A data preprocessing device: cleaning and processing an original task list data set, wherein the original task list data set comprises task lists in a plurality of iterative processes of a plurality of projects, cleaning and attribute constructing are carried out on data, and attributes comprise project names, service types, service scenes, requirement description, related applications and the like. And after reconstruction is completed, providing the data to the associated mining device.
The association mining device: a Bayesian algorithm is adopted to construct a service type classification model based on requirement description, an Apriori algorithm is adopted to mine an association set of service types and product applications, a data source is input to construct the model, support degree personalized parameters are set to control the proportion of product application sets, the algorithm model is constructed to output frequent item sets, and frequent item screening is carried out through parameter setting of support degree and confidence degree. The device is used for providing the classification model and the association rule of the product application and providing the classification model and the association rule to the division generating device.
Division generating device: and the input of product requirements is supported, and the service type of the requirements is judged through a classification model. And then, jointly inputting the system according to the association rules of the service types and the product applications and the association rules set individually, performing application division and labor recommendation on the demand items of different service types, supporting manual modification and storage, and finally outputting a demand task sheet containing the product application division and labor.
Fig. 4 is a diagram of a data preprocessing apparatus, the data preprocessing apparatus 1 includes a data acquisition unit 11, a data cleansing unit 12, an attribute construction unit 13, and a data transformation unit 14, wherein:
the data acquisition unit 11: an original task sheet in a plurality of iterative processes for importing a plurality of projects.
The data cleansing unit 12: and screening out the task list with a normal state in the iteration process, and filtering the task list with a waste state caused by errors due to work division to form a cleaned data source to be processed.
The attribute construction unit 13: the method is used for constructing the cleaned data according to the project name, the service type, the service scene, the requirement description, the related application and other attributes.
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.
Fig. 5 is an association mining apparatus, and the association mining apparatus 2 includes a data filtering unit 21, a data word segmentation unit 22, a classification model unit 23, a parameter setting unit 24, a frequent item generation unit 25, and an association rule output unit 26, where:
the data filtering unit 21: the data source is reserved only for the requirement item, the service type and the product application attribute. Table 1 is a table of examples of association between service types and product applications, and shows examples of association between data formed after data screening.
Data word segmentation unit 22: the method is used for performing word segmentation processing on the requirement description information of the requirement item to extract the business key information.
Classification model unit 23: the method is used for constructing the service classification model by using a naive Bayes classification algorithm.
The parameter setting unit 24: the method is used for setting a support degree parameter N and a confidence degree parameter M.
The frequent item generating unit 25: a frequent itemset for a set of computing product applications.
Association rule output unit 26: and the method is used for screening the frequent item set with the confidence coefficient meeting the requirement from the frequent item set, and further generating the product application association rule based on the screened frequent item set.
Fig. 6 is a diagram of a division labor generating device, the division labor generating device 3 includes a requirement input unit 31, a text typesetting unit 32, a personalization setting unit 33, and a division labor generating unit 34, wherein:
the demand entry unit 31: supporting to input one or more project requirements, judging the service type of the newly added input requirement by using a classification model, and supporting to manually adjust the service type of each requirement item;
text composition unit 32: the generated task list is typeset, two empty grids are started, and finally empty row symbols are added to perform a simple beautifying function;
the personalization setting unit 33: the method supports manual entry rules, supports maintenance of one or more product applications which do not need to be confirmed for each existing service type, supports new service types, supports manual adjustment of the existing found associated application set, and supports character modification and storage after the task list is automatically generated.
Division generating unit 34: and automatically recommending and generating a plurality of requirement task lists meeting the conditions according to the manual entry rule and the associated application discovery rule, and generating a complete requirement task list after manual modification or confirmation.
It should be noted that the above-mentioned apparatus may also include other embodiments according to the description of the above-mentioned embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The present specification also provides an electronic device that may include at least one processor and a memory storing processor-executable instructions that, when executed by the processor, perform steps comprising the method of any one or more of the embodiments described above.
The memory may include physical means for storing information, typically by digitizing the information for storage on a medium using electrical, magnetic or optical means. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
Accordingly, the present specification also provides a computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one or more of the above embodiments.
It should be noted that the embodiments of the present disclosure are not limited to the cases where the data model/template is necessarily compliant with the standard data model/template or the description of the embodiments of the present disclosure. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using these modified or transformed data acquisition, storage, judgment, processing, etc. may still fall within the scope of the alternative embodiments of the present description.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A business item processing method is applied to a server, and the method comprises the following steps:
receiving a target requirement item of a business project; the target requirement item is used for representing a sub-development link in the business project development process;
extracting service key information of the target demand item, and determining a service type to which the target demand item belongs based on the service key information to serve as a target service type;
obtaining a product application association rule corresponding to the target service type, wherein the product application association rule is used for representing a potential association relation among product applications related to the target service type;
and determining the product application associated with the target demand item based on the product application association rule so as to realize the processing of the target demand item by utilizing the determined product application.
2. The method according to claim 1, wherein the determining the service type to which the target requirement item belongs based on the service key information comprises:
and processing the service key information by utilizing a pre-constructed service classification model to obtain the service type of the target demand item.
3. The method of claim 2, wherein the traffic classification model is constructed by:
extracting a requirement item, service key information of the requirement item and a service type of the requirement item from project data of a developed service project;
for any demand item, constructing an input vector of the corresponding demand item by using the business key information of the demand item, and taking the business type of the demand item as a label of the corresponding demand item;
taking the input vector and the label of the demand item as a sample to obtain a sample set;
and constructing a business classification model by using the sample set.
4. The method of claim 3, wherein the sample set is processed using a naive Bayes classification algorithm to obtain a traffic classification model.
5. The method of claim 1, further comprising:
extracting requirement items, service types to which the requirement items belong and product applications for realizing the corresponding requirement items from the project data of the developed service projects, and taking the requirement items, the service types to which the requirement items belong and the product applications as data sources;
extracting product applications related to any service type from the data source to obtain a product application set corresponding to the corresponding service type;
and for any service type, extracting the potential association relation among product applications in a product application set corresponding to the service type to obtain a product application association rule of the corresponding service type.
6. The method according to claim 5, wherein Apriori algorithm is used to extract the potential association relationship between product applications in the product application set corresponding to the service type.
7. The method according to claim 6, wherein the extracting, by using Apriori algorithm, the potential association relationship between the product applications in the product application set corresponding to the service type includes:
extracting a frequent item set of the product application set under the k item set; wherein the k item set refers to a set containing k product applications in the product application set; the initial value of k is 1, k is a positive integer less than or equal to n-1, and n is the total product application number of the product application set;
constructing a k +1 item set of the product application set based on the frequent item set under the k item set, and extracting the frequent item set of the k +1 item set;
executing the iteration steps until the value of k is equal to n-1, and outputting a frequent item set of the product application set under each item number;
screening out a frequent item set with the confidence coefficient larger than the confidence coefficient parameter from the frequent item sets under the terms;
and determining potential association relations among the product applications in the product application set based on the screened frequent item sets.
8. A business item processing apparatus, characterized in that the apparatus comprises:
the receiving module is used for receiving a target demand item of a business project; the target requirement item is used for representing a sub-development link in the business project development process;
the extraction module is used for extracting the business key information of the target demand item, determining the business type of the target demand item based on the business key information and taking the business type as a target business type;
the association rule obtaining module is used for obtaining a product application association rule corresponding to the target service type, and the product application association rule is used for representing a potential association relation among product applications related to the target service type;
and the association application determination module is used for determining the product application associated with the target requirement item based on the product application association rule so as to realize the processing of the target requirement item by utilizing the determined product application.
9. An electronic device comprising at least one processor and a memory storing processor-executable instructions that, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium having computer instructions stored thereon which, when executed, implement the steps of the method of any one of claims 1 to 7.
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| CN202110590002.0A CN113159738A (en) | 2021-05-28 | 2021-05-28 | Business item processing method and device, electronic equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114816577A (en) * | 2022-05-11 | 2022-07-29 | 平安普惠企业管理有限公司 | Method, device, electronic equipment and medium for configuring service platform function |
| CN116611793A (en) * | 2023-06-14 | 2023-08-18 | 中国长江三峡集团有限公司 | Service data induction method and system based on feature analysis |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114816577A (en) * | 2022-05-11 | 2022-07-29 | 平安普惠企业管理有限公司 | Method, device, electronic equipment and medium for configuring service platform function |
| CN116611793A (en) * | 2023-06-14 | 2023-08-18 | 中国长江三峡集团有限公司 | Service data induction method and system based on feature analysis |
| CN116611793B (en) * | 2023-06-14 | 2024-04-16 | 中国长江三峡集团有限公司 | Service data induction method and system based on feature analysis |
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