CN114943474A - Research and development workload detection method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to artificial intelligence and provides a research and development workload detection method, a research and development workload detection device, research and development workload detection equipment and a storage medium. The method includes the steps of reading a research and development demand text of a research and development system, dividing the research and development demand text into a plurality of initial stories if the research and development demand text passes quality detection, conducting vectorization processing on the plurality of initial stories according to text vocabularies in each initial story to obtain story vectors, combining the plurality of initial stories according to story similarity generated by the story vectors to obtain reasonable and accurate user stories, and therefore the number of stories can be accurately counted to serve as feedback results. Furthermore, the invention also relates to a block chain technique, and the feedback result can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a research and development workload detection method, a research and development workload detection device, research and development workload detection equipment and a storage medium.
Background
In the current research and development management process, a requirement analyst or a system administrator generally performs personalized splitting on a research and development requirement text according to the skill level, the task load degree and the company performance assessment requirement of each member in a research and development team, however, the method cannot reasonably and accurately divide the research and development requirement text, so that the number of user stories obtained by dividing cannot be accurately counted, and balanced distribution of tasks cannot be achieved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a device and a storage medium for detecting research and development workload, which can solve the technical problem of how to reasonably and accurately divide research and development requirement texts to improve the detection of the research and development workload.
On one hand, the invention provides a research and development workload detection method, which comprises the following steps:
when a research and development workload detection request is received, reading a research and development requirement text of a research and development system according to the research and development workload detection request;
identifying whether the research and development requirement text passes quality detection;
if the research and development requirement text passes the quality detection, dividing the research and development requirement text into a plurality of initial stories;
vectorizing the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story;
merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
According to a preferred embodiment of the present invention, the reading of the development requirement text of the development system according to the development workload detection request includes:
analyzing a document storage path and a system identification from the quality detection request;
determining a development system corresponding to the system identification as the development system;
identifying a current version of the development system;
positioning a document with a publication number larger than the current version and corresponding to the system identifier from the document storage path as a document to be detected;
and reading the research and development requirement text from the document to be detected.
According to a preferred embodiment of the present invention, the identifying whether the development requirement text passes the quality detection includes:
comparing the text vocabulary quantity of the research and development requirement text with a preset vocabulary quantity;
if the number of the text vocabularies is larger than or equal to the preset number of the vocabularies, detecting the research and development required text based on a pre-trained text scoring model to obtain a text score of the research and development required text;
and if the text score is greater than or equal to a preset score, determining that the research and development requirement text passes quality detection.
According to a preferred embodiment of the present invention, the text scoring model includes a background field scoring network, a system name scoring network, and a detail scoring network, and the detecting the research and development demand text based on the pre-trained text scoring model to obtain the text score of the research and development demand text includes:
dividing the research and development requirement text to obtain a background description text, a field description text, a system description text and a detail description text;
inputting the background description text and the field description text into the background field scoring network for detection to obtain a first score;
inputting the system description text into the system name scoring network for detection to obtain a second score;
inputting the detail description text into the detail scoring network for detection to obtain a third score;
obtaining a first weight of the background field scoring network, a second weight of the system name scoring network and a third weight of the detail scoring network from the text scoring model;
and carrying out weighting and operation processing on the first score, the second score and the third score based on the first weight, the second weight and the third weight to obtain the text score.
According to a preferred embodiment of the present invention, the dividing the development requirement text into a plurality of initial stories includes:
acquiring a preset regular expression;
writing preset characters into the preset regular expression to obtain a division expression;
running the division expression on the research and development requirement text to obtain a plurality of division sentences;
determining the plurality of division sentences as the plurality of initial stories.
According to a preferred embodiment of the present invention, the vectorizing of the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story includes:
performing word segmentation processing on each initial story to obtain a plurality of initial words;
removing stop words in the plurality of initial words based on a preset stop word list to obtain the text words;
acquiring a vocabulary vector of each text vocabulary from a preset vector mapping table;
and calculating the average value of the vocabulary vector on each vector dimension to obtain the story vector.
According to a preferred embodiment of the present invention, the merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story includes:
identifying a text position of each initial story in the development requirement text;
selecting any adjacent initial story from the plurality of initial stories as a story pair according to the text position;
calculating the story similarity of the story pair according to the story vector;
and in the plurality of initial stories, iteratively combining story pairs with story similarity larger than a preset similarity threshold value to obtain the at least one user story.
In another aspect, the present invention further provides a research and development workload detection apparatus, where the research and development workload detection apparatus includes:
the reading unit is used for reading a research and development requirement text of a research and development system according to a research and development workload detection request when the research and development workload detection request is received;
the identification unit is used for identifying whether the research and development requirement text passes quality detection;
the dividing unit is used for dividing the research and development requirement text into a plurality of initial stories if the research and development requirement text passes quality detection;
the processing unit is used for carrying out vectorization processing on the plurality of initial stories according to the text vocabularies in each initial story to obtain story vectors of each initial story;
the merging unit is used for merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and the statistical unit is used for counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
In another aspect, the present invention further provides an electronic device, including:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the development workload detection method.
In another aspect, the present invention further provides a computer-readable storage medium, in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the research and development workload detection method.
According to the technical scheme, the quality of the research and development demand text is detected, the research and development demand text is directly divided after the research and development demand text passes through the quality detection, the dividing efficiency is improved, the research and development demand text with unqualified quality can be prevented from being analyzed, the waste of analysis time is avoided, furthermore, the initial stories are subjected to vectorization analysis through the text vocabulary in each initial story, the characterization capacity of story vectors is improved, the story similarity generated through the story vectors can accurately realize the combination of the initial stories, the combination accuracy is improved, the separation rationality and the accuracy of the stories of a user can be improved by the method of combining the research and development demand text after splitting the stories, and the accuracy of the story quantity is improved, and further, the balanced distribution of tasks can be realized.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a workload detection method of the present invention.
FIG. 2 is a functional block diagram of a workload detection apparatus according to a preferred embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device implementing a method for detecting a research workload according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a workload detection method according to a preferred embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The research and development workload detection method can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The research and development workload detection method is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to computer readable instructions set in advance or stored in advance, and hardware of the electronic devices includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), a smart wearable device, and the like.
The electronic device may include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network electronic device, an electronic device group consisting of a plurality of network electronic devices, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network electronic devices.
The network in which the electronic device is located includes, but is not limited to: the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, when receiving the research and development workload detection request, reading the research and development requirement text of the research and development system according to the research and development workload detection request.
In at least one embodiment of the present invention, the research and development workload detection request may be generated by a user who allocates a development task of the research and development system, and the research and development workload detection request may also be generated by a task allocation system after receiving the research and development requirement text.
The development system refers to a system which needs to be developed for the first time or for the second time.
The research and development requirement text may be information recorded in a product requirement document corresponding to the research and development system, and the research and development requirement text may also be information recorded in a technical requirement document corresponding to the research and development system.
In at least one embodiment of the present invention, the reading, by the electronic device, a development requirement text of a development system according to the development workload detection request includes:
analyzing a document storage path and a system identifier from the quality detection request;
determining a development system corresponding to the system identification as the development system;
identifying a current version of the development system;
positioning a document with a publication number larger than the current version and corresponding to the system identifier from the document storage path as a document to be detected;
and reading the research and development requirement text from the document to be detected.
Wherein the document storage path may be a storage path of the task allocation system.
The system identification refers to an identification code capable of uniquely identifying the research and development system.
The current version is a version number corresponding to the research and development system when receiving the research and development workload detection request.
The research and development system can be accurately positioned through the system identification, so that the identification accuracy of the current version is improved, the current version and the system identification are further combined, the document to be detected can be accurately acquired from the document storage path, and the accuracy of the research and development requirement text is improved.
And S11, identifying whether the development requirement text passes the quality detection.
In at least one embodiment of the invention, the electronic device identifying whether the development requirement text passes the quality detection comprises:
comparing the text vocabulary quantity of the research and development requirement text with a preset vocabulary quantity;
if the number of the text vocabularies is larger than or equal to the preset number of the vocabularies, detecting the research and development required text based on a pre-trained text scoring model to obtain a text score of the research and development required text;
and if the text score is greater than or equal to a preset score, determining that the research and development requirement text passes quality detection.
Wherein, the text vocabulary number refers to the total word number of the research and development requirement text.
The preset vocabulary number may be determined according to an average word count of the demand documents through quality detection.
The text scoring model comprises a background field scoring network, a system name scoring network and a detail scoring network.
The preset score can be set according to actual requirements. For example, the preset score may be 80.
By combining the comparison between the text vocabulary quantity and the preset vocabulary quantity and the comparison between the text score and the preset score, whether the research and development requirement text passes the quality detection can be accurately detected.
Specifically, the detecting, by the electronic device, the research and development requirement text based on a pre-trained text scoring model, and obtaining a text score of the research and development requirement text includes:
dividing the research and development requirement text to obtain a background description text, a field description text, a system description text and a detail description text;
inputting the background description text and the field description text into the background field scoring network for detection to obtain a first score;
inputting the system description text into the system name scoring network for detection to obtain a second score;
inputting the detail description text into the detail scoring network for detection to obtain a third score;
obtaining a first weight of the background field scoring network, a second weight of the system name scoring network and a third weight of the detail scoring network from the text scoring model;
and carrying out weighting and operation processing on the first score, the second score and the third score based on the first weight, the second weight and the third weight to obtain the text score.
The first score may be determined by calculating a similarity between the background description text and the field description text through the background field scoring network.
The second score may be determined by a matching degree between a storage system name in the system name scoring network and the system description text.
The third score can be obtained by determining the description definition of the service rule and the service flow of the detail description text by the detail scoring network.
The background description text, the field description text, the system description text and the detail description text are respectively detected through the background field scoring network, the system name scoring network and the detail scoring network, so that the accuracy of the first score, the second score and the third score can be improved, the first score, the second score and the third score are weighted and operated in combination with the first weight, the second weight and the third weight, and the accuracy of the text score can be further improved.
In other embodiments, the method further comprises:
and if the text vocabulary number is smaller than the preset vocabulary number or the text score is smaller than the preset score, determining that the research and development required text fails in quality detection, and generating alarm information according to the text score.
Through the implementation mode, the alarm information can be generated in time so as to remind a user of modifying the research and development requirement text.
S12, if the research and development requirement text passes the quality detection, dividing the research and development requirement text into a plurality of initial stories.
In at least one embodiment of the invention, each initial story corresponds to each divided sentence in the development requirement text.
In at least one embodiment of the present invention, the electronic device dividing the development requirement text into a plurality of initial stories includes:
acquiring a preset regular expression;
writing preset characters into the preset regular expression to obtain a division expression;
running the division expression on the research and development requirement text to obtain a plurality of division sentences;
determining the plurality of division sentences as the plurality of initial stories.
Wherein the division expression may be: pattern ═ r' [. |! ]'. The plurality of division sentences may be in the development requirement text. ""! "ending sentence.
The division expressions can be generated rapidly through the preset regular expressions, and then the division expressions can be used for extracting the division sentences rapidly, so that the division efficiency of the initial stories is improved.
And S13, performing vectorization processing on the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story.
In at least one embodiment of the invention, the text vocabulary refers to vocabulary information obtained after performing word segmentation processing on each initial story.
The story vectors are used to characterize corresponding initial stories.
In at least one embodiment of the present invention, the electronic device vectorizes the plurality of initial stories according to the text vocabulary in each initial story, and obtaining the story vector of each initial story includes:
performing word segmentation processing on each initial story to obtain a plurality of initial words;
removing stop words in the plurality of initial words based on a preset stop word list to obtain the text words;
acquiring a vocabulary vector of each text vocabulary from a preset vector mapping table;
and calculating the average value of the vocabulary vector on each vector dimension to obtain the story vector.
The preset deactivation word list may be a deactivation word list acquired from the internet. The stop words stored in the preset stop word list are words without semantic information.
And the preset vector mapping table stores the mapping relation between a plurality of words and vectors. And the dimension of the characterization vector of each vocabulary in the preset vector mapping table is the same.
By removing stop words from the plurality of initial words, the acquisition amount of the word vector can be reduced, and thus the generation efficiency of the story vector is improved.
And S14, merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story.
In at least one embodiment of the invention, the story similarity refers to a similarity of any two adjacent initial stories of the plurality of initial stories.
The similarity of any adjacent user stories in the at least one user story is less than or equal to a preset similarity threshold. The preset similarity threshold value can be set according to actual requirements.
In at least one embodiment of the present invention, the electronic device merging the plurality of initial stories according to the story similarity generated by the story vector, and obtaining at least one user story comprises:
identifying a text position of each initial story in the development requirement text;
selecting any adjacent initial story from the plurality of initial stories as a story pair according to the text position;
calculating the story similarity of the story pair according to the story vector;
and in the plurality of initial stories, iteratively combining story pairs with story similarity larger than a preset similarity threshold value to obtain the at least one user story.
By identifying the text position, the generation accuracy of the story pair can be improved, and further by performing iterative combination on the initial stories according to the story similarity of the story pair, the adjacent user stories in the at least one user story can be ensured to be smaller than or equal to the preset similarity threshold, so that the generation rationality and the generation accuracy of the at least one user story are improved.
Wherein, the calculation formula of the story similarity is as follows:
wherein sim (a, b) represents the story similarity, a represents the story vector of the first story in the story pair, b represents the story vector of the second story in the story pair, n represents the total number of dimensions of the story dimensions, a i An ith vector dimension, b, of a story vector representing the first story i An ith vector dimension of the story vectors representing the second story.
The accuracy of the story similarity can be improved through the operation result of the ith vector dimension in the story vector of the first story and the ith vector dimension in the story vector of the second story.
S15, counting the story quantity of the at least one user story as the feedback result of the research and development workload detection request.
It is emphasized that the feedback result may also be stored in a node of a block chain in order to further ensure the privacy and security of the feedback result.
In at least one embodiment of the present invention, the story number refers to a total number of stories of the at least one user.
In at least one embodiment of the present invention, after counting the story number of the at least one user story as a feedback result of the development workload detection request, the method includes:
and sending the feedback result to a task allocation system triggering the generation of the research and development workload detection request.
According to the technical scheme, the quality of the research and development demand text is detected, the research and development demand text is directly divided after the research and development demand text passes the quality detection, the dividing efficiency is improved, the research and development demand text with unqualified quality can be prevented from being analyzed, the waste of analysis time is avoided, furthermore, the initial stories are subjected to vectorization analysis through the text vocabulary in each initial story, the characterization capability of story vectors is improved, the merging of the initial stories can be accurately realized through the story similarity generated by the story vectors, the merging accuracy is improved, the splitting reasonability and the accuracy of the stories of a user can be improved through the mode of splitting and merging the research and development demand text, and the accuracy of the story quantity is improved, and further, the balanced distribution of tasks can be realized.
Fig. 2 is a functional block diagram of a workload detection apparatus according to a preferred embodiment of the present invention. The development workload detection apparatus 11 includes a reading unit 110, a recognition unit 111, a dividing unit 112, a processing unit 113, a merging unit 114, a counting unit 115, and a sending unit 116. The module/unit referred to herein is a series of computer readable instruction segments that can be accessed by the processor 13 and perform a fixed function and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
When receiving a research and development workload detection request, the reading unit 110 reads a research and development requirement text of a research and development system according to the research and development workload detection request.
In at least one embodiment of the present invention, the research and development workload detection request may be generated by a user who allocates a development task of the research and development system, and the research and development workload detection request may also be generated by a task allocation system after receiving the research and development requirement text.
The development system refers to a system which needs to be developed for the first time or for the second time.
The research and development requirement text may be information recorded in a product requirement document corresponding to the research and development system, and the research and development requirement text may also be information recorded in a technical requirement document corresponding to the research and development system.
In at least one embodiment of the present invention, the reading unit 110 reads the development requirement text of the development system according to the development workload detection request, where the reading unit includes:
analyzing a document storage path and a system identifier from the quality detection request;
determining a development system corresponding to the system identification as the development system;
identifying a current version of the development system;
positioning a document with a publication number larger than the current version and corresponding to the system identifier from the document storage path as a document to be detected;
and reading the research and development requirement text from the document to be detected.
Wherein the document storage path may be a storage path of the task distribution system.
The system identification refers to an identification code capable of uniquely identifying the research and development system.
The current version is a version number corresponding to the research and development system when receiving the research and development workload detection request.
The research and development system can be accurately positioned through the system identification, so that the identification accuracy of the current version is improved, the current version and the system identification are further combined, the document to be detected can be accurately acquired from the document storage path, and the accuracy of the research and development requirement text is improved.
The identifying unit 111 identifies whether the development requirement text passes the quality test.
In at least one embodiment of the present invention, the identifying unit 111 identifies whether the development requirement text passes the quality detection, including:
comparing the text vocabulary quantity of the research and development requirement text with a preset vocabulary quantity;
if the number of the text vocabularies is larger than or equal to the preset number of the vocabularies, detecting the research and development required text based on a pre-trained text scoring model to obtain a text score of the research and development required text;
and if the text score is greater than or equal to a preset score, determining that the research and development requirement text passes quality detection.
Wherein the text vocabulary number refers to the total word number of the development requirement text.
The preset vocabulary number may be determined according to an average word count of the demand documents through quality detection.
The text scoring model comprises a background field scoring network, a system name scoring network and a detail scoring network.
The preset score can be set according to actual requirements. For example, the preset score may be 80.
By combining the comparison between the text vocabulary quantity and the preset vocabulary quantity and the comparison between the text score and the preset score, whether the research and development requirement text passes the quality detection can be accurately detected.
Specifically, the detecting, by the identifying unit 111, the research and development requirement text based on a text scoring model trained in advance, and obtaining the text score of the research and development requirement text includes:
dividing the research and development requirement text to obtain a background description text, a field description text, a system description text and a detail description text;
inputting the background description text and the field description text into the background field scoring network for detection to obtain a first score;
inputting the system description text into the system name scoring network for detection to obtain a second score;
inputting the detail description text into the detail scoring network for detection to obtain a third score;
acquiring a first weight of the background field scoring network, a second weight of the system name scoring network and a third weight of the detail scoring network from the text scoring model;
and carrying out weighting and operation processing on the first score, the second score and the third score based on the first weight, the second weight and the third weight to obtain the text score.
The first score may be determined by calculating a similarity between the background description text and the field description text through the background field scoring network.
The second score may be determined by a matching degree between a storage system name in the system name scoring network and the system description text.
The third score can be obtained by determining the description definition of the detail description text and the service flow through the detail scoring network.
The background description text, the field description text, the system description text and the detail description text are respectively detected through the background field scoring network, the system name scoring network and the detail scoring network, so that the accuracy of the first score, the second score and the third score can be improved, the first score, the second score and the third score are weighted and operated in combination with the first weight, the second weight and the third weight, and the accuracy of the text score can be further improved.
In other embodiments, if the text vocabulary quantity is smaller than the preset vocabulary quantity or the text score is smaller than the preset score, the identifying unit 111 determines that the research and development required text fails in quality detection, and generates an alarm message according to the text score.
Through the implementation mode, the alarm information can be generated in time so as to remind a user of modifying the research and development requirement text.
If the development requirement text passes the quality detection, the dividing unit 112 divides the development requirement text into a plurality of initial stories.
In at least one embodiment of the invention, each initial story corresponds to each divided sentence in the development requirement text.
In at least one embodiment of the present invention, the dividing unit 112 divides the development requirement text into a plurality of initial stories including:
acquiring a preset regular expression;
writing preset characters into the preset regular expression to obtain a division expression;
running the division expression on the research and development requirement text to obtain a plurality of division sentences;
determining the plurality of division sentences as the plurality of initial stories.
Wherein the division expression may be: pattern ═ r' [. |! ]'. The plurality of division sentences may be in the development requirement text. ""! "statement of the end.
The division expressions can be generated rapidly through the preset regular expressions, and then the division expressions can be used for extracting the division sentences rapidly, so that the division efficiency of the initial stories is improved.
The processing unit 113 performs vectorization processing on the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story.
In at least one embodiment of the invention, the text vocabulary refers to vocabulary information obtained after performing word segmentation processing on each initial story.
The story vectors are used to characterize corresponding initial stories.
In at least one embodiment of the present invention, the processing unit 113 performs vectorization processing on the multiple initial stories according to the text vocabulary in each initial story, and obtaining the story vector of each initial story includes:
performing word segmentation processing on each initial story to obtain a plurality of initial words;
removing stop words in the plurality of initial words based on a preset stop word list to obtain the text words;
acquiring a vocabulary vector of each text vocabulary from a preset vector mapping table;
and calculating the average value of the vocabulary vector on each vector dimension to obtain the story vector.
The preset deactivation word list may be a deactivation word list acquired from the internet. The stop words stored in the preset stop word list are words without semantic information.
And the preset vector mapping table stores the mapping relation between a plurality of words and vectors. And the dimension of the characterization vector of each vocabulary in the preset vector mapping table is the same.
By removing stop words from the plurality of initial words, the acquisition amount of the word vector can be reduced, and thus the generation efficiency of the story vector is improved.
The merging unit 114 merges the plurality of initial stories according to the story similarity generated by the story vector, so as to obtain at least one user story.
In at least one embodiment of the invention, the story similarity refers to a similarity of any two adjacent initial stories of the plurality of initial stories.
The similarity of any adjacent user stories in the at least one user story is less than or equal to a preset similarity threshold. The preset similarity threshold value can be set according to actual requirements.
In at least one embodiment of the present invention, the merging unit 114 merges the plurality of initial stories according to the story similarity generated by the story vector, and obtaining at least one user story includes:
identifying a text position of each initial story in the development requirement text;
selecting any adjacent initial story from the plurality of initial stories as a story pair according to the text position;
calculating the story similarity of the story pair according to the story vector;
and in the plurality of initial stories, iteratively combining story pairs with the story similarity larger than a preset similarity threshold value to obtain the at least one user story.
By identifying the text position, the generation accuracy of the story pair can be improved, and further by performing iterative combination on the initial stories according to the story similarity of the story pair, the adjacent user stories in the at least one user story can be ensured to be smaller than or equal to the preset similarity threshold, so that the generation rationality and the generation accuracy of the at least one user story are improved.
Wherein, the calculation formula of the story similarity is as follows:
wherein sim (a, b) represents the story similarity, a represents the story vector of the first story in the story pair, b represents the story vector of the second story in the story pair, n represents the total number of dimensions of the story dimensions, a i Represents the aboveThe ith vector dimension in the story vector of the first story, b i An ith vector dimension of the story vectors representing the second story.
The accuracy of the story similarity can be improved through the operation result of the ith vector dimension in the story vector of the first story and the ith vector dimension in the story vector of the second story.
The counting unit 115 counts the story number of the at least one user story as a feedback result of the development workload detection request.
It is emphasized that the feedback result may also be stored in a node of a block chain in order to further ensure the privacy and security of the feedback result.
In at least one embodiment of the present invention, the story number refers to a total number of stories of the at least one user.
In at least one embodiment of the present invention, after counting the story quantity of the at least one user story as a feedback result of the development workload detection request, the sending unit 116 sends the feedback result to a task allocation system that triggers generation of the development workload detection request.
According to the technical scheme, the quality of the research and development demand text is detected, the research and development demand text is directly divided after the research and development demand text passes through the quality detection, the dividing efficiency is improved, the research and development demand text with unqualified quality can be prevented from being analyzed, the waste of analysis time is avoided, furthermore, the initial stories are subjected to vectorization analysis through the text vocabulary in each initial story, the characterization capacity of story vectors is improved, the story similarity generated through the story vectors can accurately realize the combination of the initial stories, the combination accuracy is improved, the separation rationality and the accuracy of the stories of a user can be improved by the method of combining the research and development demand text after splitting the stories, and the accuracy of the story quantity is improved, and further, the balanced distribution of tasks can be realized.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for detecting research and development workload.
In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and computer readable instructions, such as a development workload detection program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.
The Processor 13 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The processor 13 is an operation core and a control center of the electronic device 1, and is connected to each part of the whole electronic device 1 by various interfaces and lines, and executes an operating system of the electronic device 1 and various installed application programs, program codes, and the like.
Illustratively, the computer readable instructions may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used for describing the execution process of the computer readable instructions in the electronic device 1. For example, the computer readable instructions may be divided into a reading unit 110, a recognition unit 111, a dividing unit 112, a processing unit 113, a merging unit 114, a counting unit 115, and a transmitting unit 116.
The memory 12 may be used for storing the computer readable instructions and/or modules, and the processor 13 implements various functions of the electronic device 1 by executing or executing the computer readable instructions and/or modules stored in the memory 12 and invoking data stored in the memory 12. The memory 12 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. The memory 12 may include non-volatile and volatile memories, such as: a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other storage device.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the memory 12 may be a memory having a physical form, such as a memory stick, a TF Card (Trans-flash Card), or the like.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by hardware that is configured to be instructed by computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer readable instructions comprise computer readable instruction code which may be in source code form, object code form, an executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying said computer readable instruction code, a recording medium, a U disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM).
The block chain is a novel application mode of computer technologies such as distributed research and development workload detection, point-to-point transmission, consensus mechanism, encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
With reference to fig. 1, the memory 12 of the electronic device 1 stores computer-readable instructions to implement a development workload detection method, and the processor 13 can execute the computer-readable instructions to implement:
when a research and development workload detection request is received, reading a research and development requirement text of a research and development system according to the research and development workload detection request;
identifying whether the research and development requirement text passes quality detection;
if the research and development requirement text passes the quality detection, dividing the research and development requirement text into a plurality of initial stories;
vectorizing the plurality of initial stories according to the text vocabularies in each initial story to obtain story vectors of each initial story;
merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer readable instructions, which is not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The computer readable storage medium has computer readable instructions stored thereon, wherein the computer readable instructions when executed by the processor 13 are configured to implement the steps of:
when a research and development workload detection request is received, reading a research and development requirement text of a research and development system according to the research and development workload detection request;
identifying whether the research and development requirement text passes quality detection;
if the research and development requirement text passes the quality detection, dividing the research and development requirement text into a plurality of initial stories;
vectorizing the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story;
merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The plurality of units or devices may also be implemented by one unit or device through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A research and development workload detection method is characterized by comprising the following steps:
when a research and development workload detection request is received, reading a research and development requirement text of a research and development system according to the research and development workload detection request;
identifying whether the research and development requirement text passes quality detection;
if the research and development requirement text passes the quality detection, dividing the research and development requirement text into a plurality of initial stories;
vectorizing the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story;
merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
2. The research and development workload detection method according to claim 1, wherein the reading of the research and development requirement text of the research and development system according to the research and development workload detection request comprises:
analyzing a document storage path and a system identification from the quality detection request;
determining a development system corresponding to the system identification as the development system;
identifying a current version of the development system;
positioning a document with a publication number larger than the current version and corresponding to the system identifier from the document storage path as a document to be detected;
and reading the research and development requirement text from the document to be detected.
3. The development workload detection method according to claim 1, wherein the identifying whether the development requirement text passes the quality detection comprises:
comparing the text vocabulary quantity of the research and development requirement text with a preset vocabulary quantity;
if the number of the text vocabularies is larger than or equal to the preset number of the vocabularies, detecting the research and development required text based on a pre-trained text scoring model to obtain a text score of the research and development required text;
and if the text score is greater than or equal to a preset score, determining that the research and development requirement text passes quality detection.
4. The research and development workload detection method according to claim 3, wherein the text scoring model includes a background field scoring network, a system name scoring network, and a detail scoring network, and the detecting the research and development requirement text based on the pre-trained text scoring model to obtain the text score of the research and development requirement text includes:
dividing the research and development requirement text to obtain a background description text, a field description text, a system description text and a detail description text;
inputting the background description text and the field description text into the background field scoring network for detection to obtain a first score;
inputting the system description text into the system name scoring network for detection to obtain a second score;
inputting the detail description text into the detail scoring network for detection to obtain a third score;
obtaining a first weight of the background field scoring network, a second weight of the system name scoring network and a third weight of the detail scoring network from the text scoring model;
and carrying out weighting and operation processing on the first score, the second score and the third score based on the first weight, the second weight and the third weight to obtain the text score.
5. The development workload detection method of claim 1, wherein the dividing the development requirement text into a plurality of initial stories comprises:
acquiring a preset regular expression;
writing preset characters into the preset regular expression to obtain a division expression;
running the division expression on the research and development requirement text to obtain a plurality of division sentences;
determining the plurality of division sentences as the plurality of initial stories.
6. The research and development workload detection method of claim 1, wherein the vectorizing the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector for each initial story comprises:
performing word segmentation processing on each initial story to obtain a plurality of initial words;
removing stop words in the plurality of initial words based on a preset stop word list to obtain the text words;
acquiring a vocabulary vector of each text vocabulary from a preset vector mapping table;
and calculating the average value of the vocabulary vector on each vector dimension to obtain the story vector.
7. The research and development workload detection method of claim 1, wherein the merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story comprises:
identifying a text position of each initial story in the development requirement text;
selecting any adjacent initial story from the plurality of initial stories as a story pair according to the text position;
calculating the story similarity of the story pair according to the story vector;
and in the plurality of initial stories, iteratively combining story pairs with story similarity larger than a preset similarity threshold value to obtain the at least one user story.
8. A research and development workload detection apparatus, characterized by comprising:
the reading unit is used for reading a research and development requirement text of a research and development system according to a research and development workload detection request when the research and development workload detection request is received;
the identification unit is used for identifying whether the research and development requirement text passes quality detection;
the dividing unit is used for dividing the research and development requirement text into a plurality of initial stories if the research and development requirement text passes quality detection;
the processing unit is used for carrying out vectorization processing on the plurality of initial stories according to the text vocabulary in each initial story to obtain a story vector of each initial story;
the merging unit is used for merging the plurality of initial stories according to the story similarity generated by the story vector to obtain at least one user story;
and the statistical unit is used for counting the story quantity of the at least one user story as a feedback result of the research and development workload detection request.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the development workload detection method of any of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer readable storage medium has stored therein computer readable instructions which are executed by a processor in an electronic device to implement the development workload detection method according to any one of claims 1 to 7.
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