CN108681443B - Task classification method, system and computer storage medium - Google Patents
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Abstract
A task classification method, a system and a computer storage medium are provided, wherein the method comprises the following steps: receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics; matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review; if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, the target technical task is identified as the technical task needing technical review.
Description
Technical Field
The invention relates to the technical field of computer application software development, in particular to a task classification method, a task classification system and a computer storage medium.
Background
Technical review typically involves a group of reviewers carefully reviewing the software requirements, designs, code or other technical documentation in accordance with the normative procedures to find and remove defects therein. Technical review provides a training way for software analysis, design and implementation for novices, and backup and subsequent developers can also review software developed by others through regular technology.
In the prior art, a user firstly analyzes the requirement in the software development process, and then provides technical evaluation according to the analysis result and experience judgment of the requirement. The evaluation mode judges whether evaluation is needed or not through technical experience, and is incapable of intelligentizing and low in efficiency.
Disclosure of Invention
The invention mainly aims to provide a task classification method, a task classification system and a computer storage medium, and aims to solve the technical problems that the evaluation method in the prior art cannot be intelligentized and is low in efficiency.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides a task classification method, including the following steps:
receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics, and the technical characteristics are key characteristics for describing technical operations to be executed in the target technical task;
matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
As a further preferred aspect of the present invention, the successful matching between the target technical task and any technical task category in the pre-stored technical review feature library specifically includes:
comparing each first technical feature contained in the target technical task with each second technical feature contained in each technical task category in the technical review feature library one by one;
when any one first technical feature is compared with any one second technical feature in any one technical task category, adding 1 to the matching value of the target technical task and the corresponding technical task category;
and when the matching value between the target technical task and any technical task category in the technical review feature library exceeds a second preset threshold value, the matching is considered to be successful.
As a further preferable aspect of the present invention, the method further comprises:
and when the target technical task is successfully matched with a plurality of technical task categories in the pre-stored technical review feature library, adding the target technical task into the technical task category with the highest matching value with the target technical task.
As a further preferable aspect of the present invention, the method further comprises:
acquiring each technical task of a plurality of existing technical projects, and classifying all the acquired technical tasks into K technical task categories according to the acquired coincidence degree among the technical tasks, wherein K is an integer and is smaller than the total number of the technical tasks;
and respectively calculating the occupation ratio of each of the K technical task categories including the specific task, and adding the technical task category of which the occupation ratio exceeds the first preset threshold value into the technical review feature library.
As a further preferable solution of the present invention, classifying all the acquired technical tasks into K technical task categories according to the obtained coincidence degree between the plurality of technical tasks specifically includes:
classifying each technical task into a separate technical task category;
comparing any two technical task categories, and combining the two technical task categories with the technical task content coincidence degree reaching a third preset threshold value into a new technical task category;
after comparing all the technical task categories for one round, repeatedly executing the steps of comparing and combining any two technical task categories until K technical task categories are aggregated, wherein the coincidence degree of the technical task contents between any two technical task categories in the K technical task categories is smaller than the third preset threshold value.
As a further preferable aspect of the present invention, the method further comprises:
when an instruction which is input by a user and does not approve the judgment result of the technical evaluation is received, the current target technical task is marked as the technical task needing technical evaluation, and the current target technical task is added into the technical evaluation feature library as an independent technical task category.
On the other hand, an embodiment of the present invention further provides a task classification system, including:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a target technical task input by a user, the target technical task comprises technical characteristics, and the technical characteristics are key characteristics used for describing technical operations to be executed in the target technical task;
the matching module is used for matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and the judging module is used for identifying the target technical task as the technical task needing technical evaluation if the target technical task is successfully matched with any technical task category in the pre-stored technical evaluation feature library.
As a further preferable aspect of the present invention, the matching module further includes:
the comparison submodule is used for comparing each first technical feature contained in the target technical task with each second technical feature contained in each technical task category in the technical review feature library one by one;
a matching value operator module, configured to add 1 to a matching value of the target technical task and the corresponding technical task category when any one of the first technical features is compared with any one of the second technical features in any one of the technical task categories;
and the identification submodule is used for judging that the matching is successful when the matching value between the target technical task and any technical task category in the technical review feature library exceeds a second preset threshold value.
As a further preferable aspect of the present invention, the system further includes:
and the updating module is used for adding the target technical task into the technical task class with the highest matching value with the target technical task when the target technical task is successfully matched with the plurality of technical task classes in the pre-stored technical review feature library.
As a further preferable aspect of the present invention, the system further includes:
and the manual review module is used for identifying the current target technical task as a technical task needing technical review when receiving an instruction which is input by a user and does not approve the judgment result of the technical review, and adding the current target technical task into the technical review feature library as an independent technical task category.
The present invention also provides a computer storage medium having one or more programs stored thereon that are executable by one or more processors to perform the steps of:
receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics;
matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
The task classification method in one embodiment of the invention comprises the following steps: receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics; matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review; if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, the target technical task is identified as the technical task needing technical review, so that one embodiment of the invention can automatically and intelligently judge whether the new technical task needs to be reviewed by automatically analyzing the existing technical review task and identifying the technical review feature library, and continuously optimize the accuracy of next judgment by judging the new technical task, self-learning and continuously optimizing the next judgment.
The task classification system of one embodiment of the present invention includes: the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a target technical task input by a user, and the target technical task comprises technical characteristics; the matching module is used for matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review; and the judging module is used for identifying the target technical task as the technical task needing technical evaluation if the target technical task is successfully matched with any technical task category in the pre-stored technical evaluation feature library, so that the embodiment of the invention can automatically and intelligently judge whether the new technical task needs to be evaluated by automatically analyzing the existing technical evaluation task and identifying the technical evaluation feature library, and continuously optimize the accuracy of next judgment by judging the new technical task and self-learning.
By adopting the technical scheme, the computer storage medium can automatically and intelligently judge whether the new technical task needs to be evaluated or not by automatically analyzing the existing technical evaluation task and identifying the technical evaluation feature library, and continuously optimize the accuracy of the next judgment by judging the new technical task, self-learning and the like.
Drawings
FIG. 1 is a flowchart of a task classification method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a task classification method according to a second embodiment of the present invention;
fig. 3 is a block diagram of a task classification system according to a third embodiment of the present invention.
The objects, features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention aims to provide a task classification method which is applied to project management of software development. Specifically, in the existing user software development process, the general practice is as follows: firstly, analyzing the demand, and then judging whether to provide technical review according to the demand analysis result and experience. For example, a new project related to a remote audio-video conference service is to be started, and task items of the project comprise: the method comprises the steps of holding a meeting, outputting a demand concept, developing a service server, developing an audio server, developing a video server, developing a data server, developing a service SDK, designing a UI (user interface) of a client, developing a meeting list of the client, realizing a meeting process, realizing a midway meeting process and realizing disconnection reconnection of a meeting client. Among these tasks, if the manual process of the prior art is adopted, one considers that "development of an audio server" and "development of a video server" require technical review, while some technicians consider that "development of a data server" requires technical review, and some technicians consider that other works require technical review except "holding a project meeting" and "outputting a demand concept". Therefore, the existing mode of judging whether to need to be reviewed through artificial technical experience cannot be intelligentized, the efficiency is low, results given by different people may be greatly different, and the accuracy cannot be guaranteed.
In order to solve the above technical problems of the existing review technologies, the embodiments of the present invention aim to establish a set of standards to meet the technical review standards in the industry or companies as much as possible during the recognition technology review work, so as to improve the work efficiency and standardization of the software development and design stage.
Specifically, the task classification method of the embodiment of the invention comprises the following steps: receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics; matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review; if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, the target technical task is identified as the technical task needing technical review, so that the method can automatically and intelligently judge whether the new technical task needs to be reviewed by automatically analyzing the existing technical review task and identifying the technical review feature library, and continuously optimize the accuracy of next judgment by judging the new technical task and self-learning.
Example one
As shown in fig. 1, the method of the first embodiment of the task classification method of the present invention includes the following steps:
step S101, receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics;
specifically, in the process of project development, each project may include a plurality of technical tasks, which, as the name implies, relate to technical operations, specifically to tasks describing a certain technical operation to be performed, such as "holding an establishment meeting", "development of a business server", and the like. In practical applications, the technical task is manually input by a technician according to the description of the project, and the manually input technical task at least comprises one technical feature, which refers to a key feature for describing the technical operation to be performed (i.e. the technical operation described in the target technical task), such as "standing meeting" in "standing meeting", "business server" in "business server development".
In order to make the technical solution of the present invention better understood by those skilled in the art, it should be noted that the technical tasks mentioned in the present application refer to each specific task decomposed in the development process of the product. For example, it may be a design document that outputs a streaming media service at a certain time, where the streaming media includes a video stream and an audio stream. The technical features included in the technical task refer to keywords of the technical features related to the technical task, such as: the technical task is "outputting a design document of a streaming media service at a certain time, wherein the streaming media comprises a video stream and an audio stream". The technical task comprises the following technical characteristics: streaming media services, video streams, audio streams.
Step S102, matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
in step S102, the pre-stored technical review feature library is: the technical evaluation feature library is used for comparing and analyzing a target technical task newly input by a user to serve as a comparison standard to judge whether the new target technical task needs technical evaluation or not.
Specifically, in step S102, the meaning of presetting the technical review feature library is as follows: the technical tasks which are once subjected to technical evaluation are subjected to statistical analysis in advance, and the common characteristics of the technical tasks which need to be subjected to technical evaluation are summarized, so that the characteristics can be used as a uniform comparison standard for inputting new technical authentication later and can be used for judging whether the new target technical task needs to be subjected to technical evaluation.
The technical review feature library may include a plurality of technical task categories, a ratio of feature tasks included in each technical task category must be greater than a preset threshold, and the specific task is a technical task that has been subject to technical review. That is, each technical task category in the technical review feature library includes more than a certain percentage of technical tasks that have been subject to technical review. The predetermined ratio is the predetermined threshold value, which is obtained according to statistics. Thus, from a statistical point of view, if a new technical task highly matches any of these technical task categories, the new technical task may be considered to belong to the technical task that requires technical review.
Step S103, if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
If the newly input target technical task is compared with the technical review feature library, and the conclusion is that the newly input target task is successfully matched with any technical task category in the technical review feature library, the new target technical task can be considered to belong to the technical task for technical review.
Specifically, in this step, the newly input target technical task may be compared with each technical task category in the technical review feature library one by one, and when any technical task category is successfully matched with the target technical task by comparison, the comparison operation may be ended, and the target technical task is identified as the technical task requiring technical review. And the matching success referred to herein means: and when the matching value between the target technical task and any technical task category in the pre-stored technical review feature library exceeds a second preset threshold value, the matching is considered to be successful. (the second preset value can be set by the designer according to the design requirement, if it can be 85%, 90%, etc.), if yes, the target technical task needs to be reviewed, otherwise, the technical task does not need to be reviewed.
Preferably, when the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, the target technical task is added into the pre-stored technical review feature library and classified into the technical task category with the highest matching degree with the target technical task. It should be noted that, when the matching degree between the target technical task and a certain technology in the pre-stored technical review feature library is greater than or equal to a preset value, the target technical task is classified into the certain technology; and when the matching degree of the target technical task and certain types of technologies in the pre-stored technical review feature library is greater than or equal to a preset value, the technical task is classified into the technology with the highest matching degree with the technical task.
By adopting the technical scheme, the task classification method disclosed by the embodiment of the invention can automatically and intelligently judge whether the new technical task needs to be evaluated or not by automatically analyzing the existing technical evaluation task and identifying the technical evaluation feature library, and continuously optimize the accuracy of the next judgment by judging the new technical task, self-learning and continuously optimizing the accuracy of the next judgment.
Example two
Fig. 2 shows a flowchart of a task classification method according to a second embodiment of the present invention. The second embodiment is basically similar to the first embodiment in technical solution, and the difference is that the second embodiment further includes: when an instruction which is input by a user and is not approved by a judgment result of the technical review is received; and manually marking the technical task as needing to be evaluated, and adding the technical task into a pre-stored technical evaluation feature library as an independent technology so as to improve the content of the technical evaluation feature library. In addition, the second embodiment also specifically describes a specific process for generating the technical review feature library.
Step S200, generating a technical review feature library in advance;
the technical review feature library can be obtained by performing statistical analysis on technical tasks of existing technical projects, and specifically, the step S201 may further include the following steps:
step S2001, each technical task of a plurality of existing technical projects is obtained, and all the obtained technical tasks are classified into K technical task categories according to the contact ratio among the obtained technical tasks;
wherein K is an integer and is less than the total number of technical tasks. The contact ratio refers to similarity or identity between technical features of each technical task obtained by comparing the technical features of each technical task, and the technical features can be obtained by analyzing the technical tasks by an automatic learning method such as a word meaning analysis method, a lexical analysis method, a sentence analysis method and the like.
In step S2011, classifying all the acquired technical tasks into K technical task categories according to the obtained overlap ratio between the technical tasks specifically includes:
firstly, classifying each technical task into an individual technical task category, namely, firstly, considering each technical task to belong to an individual technical task category;
then, comparing any two technical task categories (namely comparing any two technical tasks), and combining the two technical task categories with the technical task content coincidence degree reaching a third preset threshold value into a new technical task category;
in this step, any two technical tasks of the plurality of technical tasks are compared to judge the contact ratio between any two technical tasks. Specifically, after the technical features of each technical task are obtained through the syntax analysis method, the technical features included in the two technical tasks are compared to obtain the same and similar degrees between the technical features included in the two technical tasks. This degree of identity and similarity determines the degree of coincidence between technical tasks, the more identical the technical features, the higher the degree of coincidence. By comparing any two technical tasks, two technical task categories of which the overlap ratio of the contents of the technical tasks reaches or exceeds a preset threshold (a third preset threshold) can be combined into a new technical task category, that is, the two technical tasks (two technical task analogs) are considered to belong to one category. The third preset threshold may be specifically determined by a designer according to past experience or according to design requirements, and may be specifically an empirical value.
And finally, after one round of comparison between every two technical task categories is finished, repeatedly executing the steps of comparing and combining any two technical task categories until K technical task categories are aggregated. And the coincidence degree of the technical task contents between any two technical task categories in the K technical task categories is greater than the third preset threshold value. That is, by comparing the technical features of every two technical task categories repeatedly, all technical tasks with content repetition degrees greater than a third preset threshold value can be classified as a technical task category, and thus the finally obtained content repetition degrees between every two technical task categories are all less than the third preset threshold value. All the prior art tasks are recombined and integrated through the step, all the technical tasks with high similarity and coincidence degree are classified into one class, each obtained technical task class comprises one or more technical characteristics, and if the technical tasks comprise a plurality of technical characteristics, the technical characteristics are similar to each other.
In order to better explain the comparison between any two technical task categories and combine the two technical task categories with the technical task content coincidence degree reaching the third preset threshold into a new technical task category, the following example is given by combining two of the three technical task categories:
the technical task one is as follows: at a time, a design document of a streaming media service is output, wherein the streaming media comprises a video stream and an audio stream. The technical characteristics of the technical task extracted by the lexical analysis and statement analysis methods are as follows: streaming media services, video streams, audio streams.
And a second technical task: the audio streaming service is implemented at a certain time. The technical characteristics of the technical task obtained by the extraction of the lexical analysis method and the sentence analysis method are as follows: an audio streaming service.
And a third technical task: the update service is implemented at some time. The technical characteristics of the technical task extracted by the lexical analysis and statement analysis methods are as follows: and (6) updating the service.
And comparing the technical features extracted by the three tasks according to the permutation and combination, and calculating the value of the contact ratio, wherein the contact ratio of the technical task I and the technical task II is high, and the contact ratio of the technical task III and the technical task I or the technical task II is low. In the process of combining a new technical task category according to the coincidence degree of the technical task contents, the coincidence degree of the technical task I and the technical task II is high, and the technical task I and the technical task II reach a third preset threshold value and need to be combined into a new category; and the coincidence degree of the technical task three and the technical task one or the technical task two is low, and if the coincidence degree does not reach a third preset threshold value, the technical tasks do not need to be combined into a class. The calculation of the contact ratio may be obtained according to a preset calculation method, and the preset calculation method may be set by combining results of automatic learning methods such as a lexical analysis method and a syntactic analysis method, and a third preset threshold.
Step S2002, respectively calculating a ratio of each of the K technical task categories including a specific task, and adding the technical task category of which the ratio exceeds the first preset threshold to the technical review feature library.
After classifying the plurality of technical tasks of the prior art project through the above-described step S2001, K technical task categories are obtained, and in this step, the K technical task categories are calculated, so that the occupation ratio of each technical task category including the specific task that has been subjected to the technical review can be obtained. When the occupation ratio of any technical task class containing a specific task exceeds a first preset threshold value, the technical task class can be added into a technical review feature library, so that the technical task classes with high occupation ratios to be technically reviewed can be used as reference standards for evaluating whether the newly input technical task needs to be technically reviewed.
Step S201, receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics, and the technical characteristics for describing the technical task are input by the user;
step S202, matching the target technical task with each technical task category in a pre-stored technical review feature library one by one;
step S203, if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review, otherwise, judging that the technical task does not need to be reviewed;
in this step, whether the matching is successful or not can be determined by calculating a matching value between the target technical task and any technical task category in the technical review feature library and calculating whether the matching value exceeds a preset threshold. Specifically, as can be seen from the above description of step S200, each technical task category in the technical review feature library may include a plurality of technical features, and the technical features of the technical task categories added to the technical review feature library are all technical features that participate in technical review with a high probability, so that the newly input target technical task is matched with each technical task category in the technical review feature library one by one, specifically, the technical features in the target technical task are matched with the technical features in each technical task category in the technical review feature library one by one, and a matching value can be calculated. Therefore, according to the calculated matching value, whether the target technical task is successfully matched can be judged, and whether the target technical task is a technical task needing technical evaluation can be further judged according to the matching result.
The above-mentioned calculation process of the matching degree value can be described as follows: and comparing the whole sentence of the target technical task with a plurality of technical features in any technical task category in the technical review feature library. The method comprises the steps of performing grammatical analysis and statement analysis on a target technical task, extracting words and statements in the sentences of the target technical task, matching the words and the statements with a plurality of technical features in any technical task category one by one, and adding 1 to a matching value when the completely consistent technical features are matched until the matching is finished one by one. By matching the extracted words and sentences with each technical task category in the technical review feature library, a matching value between the target technical task and each technical task category can be obtained, and if the calculated result is that the matching value between the target technical task and any technical task category exceeds a preset threshold (a second preset threshold), the matching can be considered to be successful, so that the target technical task can be determined to be the technical task needing technical review. Conversely, if all the obtained matching values do not exceed the second preset threshold after comparing all the technical task categories in the technical review feature library one by one, the matching is considered to be unsuccessful, and the target technical task is determined to be a technical task without technical review.
In addition, if the matching result is that the matching values of the target technical task and the technical task categories in the technical review feature library all exceed the preset threshold value through comparison, the technical task category with the highest matching value is selected, and the target technical task is added into the technical review feature library, namely the technical task category with the highest matching value.
And step S204, when an instruction which is input by a user and is not approved by the judgment result of the technical review is received, manually marking the technical task as needing to be reviewed, and adding the technical task into a pre-stored technical review feature library as an independent technology so as to perfect the content of the technical review feature library.
In addition to the above-mentioned manner of determining whether the current target technical task needs to be subjected to technical review by comparing the target technical task with the technical task category in the technical review feature library, in this embodiment, in order to preserve the highest priority control of the user and improve the content of the technical review feature library, a manner of directly determining whether the technical task needs to be reviewed by the user input result is defined. Specifically, when the system receives an instruction which is input by the user and is not approved by the judgment result of the technical review, it can be considered that although the current target technical task does not need to be subjected to the technical review according to the automatic judgment of the system, the user has a mistake in the judgment result and considers that the current target technical task needs to be subjected to the technical review by the user, so that the system automatically identifies the current technical task as needing to be reviewed, and adds the current technical task as an independent technology into a pre-stored technical review feature library so as to perfect the content of the technical review feature library.
By adopting the technical scheme, the task classification method of the second embodiment has the beneficial effects of the first embodiment, namely, whether a new technical task needs to be reviewed or not can be automatically and intelligently judged by automatically analyzing the existing technical review task and identifying the technical review feature library, and the accuracy of next judgment is continuously optimized by judging the new technical task, self-learning and automatically judging the new technical task; and the highest priority control right of the user can be reserved, the content of the technical review feature library is perfected, and the accuracy of the content of the next intelligent judgment technical review is improved.
EXAMPLE III
The present invention also provides a task classification system, as shown in fig. 3, the system includes:
a receiving module 31, configured to receive a target technical task input by a user, where the target technical task includes a technical feature, and the technical feature is a key feature for describing a to-be-executed technical operation in the target technical task;
the matching module 32 is configured to match the target technical task with each technical task category in a pre-stored technical review feature library one by one, where a ratio of a specific task included in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task that has been subjected to technical review;
the judging module 33 is configured to identify the target technical task as a technical task that needs to be technically reviewed if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library.
Specifically, the matching module 32 further includes the following sub-modules:
a comparison sub-module 321, configured to compare each first technical feature included in the target technical task with each second technical feature included in each technical task category in the technical review feature library one by one;
a matching value operator module 322, configured to add 1 to the matching value of the target technical task and the corresponding technical task category when the first technical feature is compared to be consistent with any second technical feature in any technical task category;
the identifying submodule 323 is used for judging that the matching value between the target technical task and any technical task category in the technical review feature library exceeds a second preset threshold value, and then the matching is successful.
Specifically, the pre-stored technical review feature library is obtained by:
each technical task of a plurality of existing projects is obtained, all the obtained technical tasks are classified into K technical task categories according to the obtained coincidence degree of the technical tasks, and K is an integer and is smaller than the total number of the technical tasks.
Classifying all the acquired technical tasks into K technical task categories according to the obtained coincidence degree among the technical tasks specifically includes:
classifying each of the technical tasks into a separate category of the technical tasks;
comparing any two technical task categories, and combining the two technical task categories with the technical task content coincidence degree reaching a third preset threshold value into a new technical task category;
after comparing all the technical task categories for one round, repeatedly executing the steps of comparing and combining any two technical task categories until K technical task categories are aggregated, wherein the coincidence degree of the technical task contents between any two technical task categories in the K technical task categories is larger than the third preset threshold value. (this third threshold can be specifically determined by the designer based on design requirements) is stored in the technical review feature library.
Preferably, the system further comprises: and the updating module is used for adding the target technical task into the technical task class with the highest matching value with the target technical task when the target technical task is successfully matched with the plurality of technical task classes in the pre-stored technical review feature library. It should be noted that, when the matching degree between the target technical task and a certain technology in the pre-stored technical review feature library is greater than or equal to a preset value, the target technical task is classified into the certain technology; and when the matching degree of the target technical task and certain types of technologies in the pre-stored technical review feature library is greater than or equal to a preset value, the target technical task is classified into the certain type of technology with the highest matching degree with the technical task.
In order to reserve the highest priority control right of the user, the content of the technical review feature library is perfected so as to improve the accuracy of the content of the next intelligent judgment technical review. Preferably, the system further comprises: and the manual review module is used for identifying the current target technical task as a technical task needing technical review when receiving an instruction which is input by a user and does not approve the judgment result of the technical review, and adding the current target technical task into the technical review feature library as an independent technical task category.
The present invention also provides a computer storage medium having one or more programs stored thereon that are executable by one or more processors to perform the steps of:
receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics;
matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
Specifically, the pre-stored technical review feature library is obtained by:
each technical task of a plurality of existing projects is obtained, all the obtained technical tasks are classified into K technical task categories according to the obtained coincidence degree of the technical tasks, and K is an integer and is smaller than the total number of the technical tasks.
Classifying all the acquired technical tasks into K technical task categories according to the obtained coincidence degree among the technical tasks specifically includes:
classifying each of the technical tasks into a separate category of the technical tasks;
comparing any two technical task categories, and combining the two technical task categories with the technical task content coincidence degree reaching a third preset threshold value into a new technical task category;
after comparing all the technical task categories for one round, repeatedly executing the steps of comparing and combining any two technical task categories until K technical task categories are aggregated, wherein the coincidence degree of the technical task contents between any two technical task categories in the K technical task categories is larger than the third preset threshold value. (this third threshold can be specifically determined by the designer based on design requirements) is stored in the technical review feature library.
It will be appreciated that the specific number of K may be set in advance by the designer, and may be, for example, 10 or 15.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A task classification method is characterized by comprising the following steps:
acquiring each technical task of a plurality of existing technical projects, and classifying all the acquired technical tasks into K technical task categories according to the acquired coincidence degree among the technical tasks, wherein K is an integer and is smaller than the total number of the technical tasks;
respectively calculating the occupation ratio of each of the K technical task categories including a specific task, and adding the technical task category of which the occupation ratio exceeds a first preset threshold value into a technical review feature library;
receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics, and the technical characteristics are key characteristics for describing technical operations to be executed in the target technical task;
matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review; and if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
2. The task classification method according to claim 1, wherein the successful matching of the target technical task with any technical task category in the pre-stored technical review feature library specifically comprises:
comparing each first technical feature contained in the target technical task with each second technical feature contained in each technical task category in the technical review feature library one by one;
when any one first technical feature is compared with any one second technical feature in any one technical task category, adding 1 to the matching value of the target technical task and the corresponding technical task category;
and when the matching value between the target technical task and any technical task category in the technical review feature library exceeds a second preset threshold value, the matching is considered to be successful.
3. The task classification method according to claim 2, characterized in that the method further comprises:
and when the target technical task is successfully matched with a plurality of technical task categories in the pre-stored technical review feature library, adding the target technical task into the technical task category with the highest matching value with the target technical task.
4. The task classification method according to claim 1, wherein classifying all the acquired technical tasks into K technical task categories according to the obtained degree of coincidence between the technical tasks specifically comprises:
classifying each technical task into a separate technical task category;
comparing any two technical task categories, and combining the two technical task categories with the technical task content coincidence degree reaching a third preset threshold value into a new technical task category;
after comparing all the technical task categories for one round, repeatedly executing the steps of comparing and combining any two technical task categories until K technical task categories are aggregated, wherein the coincidence degree of the technical task contents between any two technical task categories in the K technical task categories is smaller than the third preset threshold value.
5. The task classification method according to claim 1, characterized in that the method further comprises:
when an instruction which is input by a user and does not approve the judgment result of the technical evaluation is received, the current target technical task is marked as the technical task needing technical evaluation, and the current target technical task is added into the technical evaluation feature library as an independent technical task category.
6. A task classification system, comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for acquiring each technical task of a plurality of existing technical projects, and classifying all the acquired technical tasks into K technical task categories according to the acquired contact ratio among the technical tasks, wherein K is an integer and is less than the total number of the technical tasks;
respectively calculating the occupation ratio of each of the K technical task categories including a specific task, and adding the technical task category of which the occupation ratio exceeds a first preset threshold value into a technical review feature library; receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics, and the technical characteristics are key characteristics for describing technical operations to be executed in the target technical task;
the matching module is used for matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and the judging module is used for identifying the target technical task as the technical task needing technical evaluation if the target technical task is successfully matched with any technical task category in the pre-stored technical evaluation feature library.
7. The task classification system of claim 6, wherein the matching module further comprises:
the comparison submodule is used for comparing each first technical feature contained in the target technical task with each second technical feature contained in each technical task category in the technical review feature library one by one;
a matching value operator module, configured to add 1 to a matching value of the target technical task and the corresponding technical task category when any one of the first technical features is compared with any one of the second technical features in any one of the technical task categories;
and the identification submodule is used for judging that the matching is successful when the matching value between the target technical task and any technical task category in the technical review feature library exceeds a second preset threshold value.
8. The task classification system of claim 6, further comprising:
and the updating module is used for adding the target technical task into the technical task class with the highest matching value with the target technical task when the target technical task is successfully matched with the plurality of technical task classes in the pre-stored technical review feature library.
9. The task classification system according to any of claims 7-8, characterized in that the system further comprises:
and the manual review module is used for identifying the current target technical task as a technical task needing technical review when receiving an instruction which is input by a user and does not approve the judgment result of the technical review, and adding the current target technical task into the technical review feature library as an independent technical task category.
10. A computer storage medium, characterized in that the computer storage medium stores one or more programs executable by one or more processors to implement the steps of:
receiving a target technical task input by a user, wherein the target technical task comprises technical characteristics;
matching the target technical task with each technical task category in a pre-stored technical review feature library one by one, wherein the occupation ratio of a specific task contained in each technical task category in the pre-stored technical review feature library exceeds a first preset threshold, and the specific task is a task which has been subjected to technical review;
and if the target technical task is successfully matched with any technical task category in the pre-stored technical review feature library, identifying the target technical task as a technical task needing technical review.
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