CN111596894A - Software requirement extraction method and device, computer equipment and readable storage medium - Google Patents

Software requirement extraction method and device, computer equipment and readable storage medium Download PDF

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CN111596894A
CN111596894A CN202010332881.2A CN202010332881A CN111596894A CN 111596894 A CN111596894 A CN 111596894A CN 202010332881 A CN202010332881 A CN 202010332881A CN 111596894 A CN111596894 A CN 111596894A
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CN111596894B (en
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胡璇
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China Electronic Product Reliability and Environmental Testing Research Institute
Maintenance and Test Branch of Peaking FM Power Generation of Southern Power Grid Co Ltd
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China Electronic Product Reliability and Environmental Testing Research Institute
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Abstract

The application relates to a software requirement extraction method, a software requirement extraction device, computer equipment and a readable storage medium. The software requirement extraction method comprises the following steps: acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts; constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts; integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software. By adopting the method, the quality of the flight control and management system software of the unmanned aerial vehicle can be improved.

Description

Software requirement extraction method and device, computer equipment and readable storage medium
Technical Field
The invention relates to the technical field of software engineering, in particular to a software requirement extraction method, a software requirement extraction device, computer equipment and a readable storage medium.
Background
The UAV FCMS (Unmanned Aerial Vehicle Flight Control and management system ) is used to realize the Control of the whole Flight process from the running, take-off, air Flight to landing approach of the Unmanned Aerial Vehicle. The unmanned aerial vehicle flight control and management system software is the core part of the UAV FCMS.
The quality of unmanned aerial vehicle flight control and management system software is the key of guarantee unmanned aerial vehicle quality, and the demand extraction activity of unmanned aerial vehicle flight control and management system software is regarded as the most important link in unmanned aerial vehicle flight control and management system software development process.
However, in the actual software development process, the quality of the developed unmanned aerial vehicle flight control and management system software is poor due to poor accuracy of software requirement extraction.
Disclosure of Invention
In view of the above, there is a need to provide a software requirement extraction method, device, computer device and readable storage medium capable of improving the quality of the flight control and management system software of the unmanned aerial vehicle.
In a first aspect, an embodiment of the present application provides a software requirement extraction method, where the software requirement extraction method includes:
acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts, wherein the ontology concepts at least comprise software and hardware concepts and an environment concept;
constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
In one embodiment, after the obtaining the integrated software requirement extraction ontology, the method further includes:
adopting a preset evaluation index to extract the body from the integrated software requirement extraction body for body quality evaluation to obtain an evaluation result; the evaluation index comprises a body cohesion degree measurement index;
and according to the evaluation result, optimizing the integrated software requirement extraction body.
In one embodiment, the integrating, according to each association relationship, the initial software requirement extraction ontology and the corresponding concept in the geographic ontology to obtain an integrated software requirement extraction ontology includes:
calculating the similarity between a plurality of ontology concepts in the initial software requirement extraction ontology and a plurality of geographic concepts in the geographic ontology according to the incidence relation between the ontology concepts and the incidence relation between the geographic concepts;
detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold value;
and if the similarity between the target ontology concept and the target geographic concept is greater than the preset threshold, merging the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology.
In one embodiment, after detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold, the method further includes:
if the similarity between the target ontology concept and the target geographic concept is not larger than the preset threshold, adopting a preset description logic strategy to detect whether a preset incidence relation exists between the target ontology concept and the target geographic concept;
and if the target ontology concept and the target geographic concept have the preset incidence relation, combining the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology.
In one embodiment, the obtaining the initial software requirement extraction ontology includes:
acquiring a generalized ontology concept space association table, and acquiring an unmanned aerial vehicle flight control and management system ontology concept dictionary table and a software demand historical defect concept dictionary table;
and constructing the initial software requirement extraction body according to the generalized body concept space association table, the unmanned aerial vehicle flight control and management system body concept dictionary table and the software requirement historical defect concept dictionary table.
In one embodiment, the obtaining a generalized ontology concept space association table includes:
acquiring a plurality of generalization concepts corresponding to the generalization ontology and an incidence relation between the generalization concepts;
and constructing the generalized ontology concept space association table based on a knowledge aided design system KADS, the plurality of generalized concepts and the association relationship among the generalized concepts.
In one embodiment, the constructing a geographic ontology according to the obtained geographic information basic data includes:
acquiring geographic information basic data, wherein the geographic information basic data are a plurality of geographic concepts selected from the field of digital geographic data and related to unmanned aerial vehicle flight control and management system software;
acquiring an incidence relation among the plurality of geographic concepts;
and constructing the geographic ontology according to the plurality of geographic concepts and the incidence relation among the plurality of geographic concepts.
In a second aspect, an embodiment of the present application provides a software requirement extraction device, where the device includes:
the acquisition module is used for acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts, wherein the ontology concepts at least comprise software and hardware concepts and an environment concept;
the construction module is used for constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
the body integration module is used for integrating the initial software requirement extraction body and the corresponding concept in the geographic body according to each incidence relation to obtain an integrated software requirement extraction body; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method according to the first aspect as described above.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
extracting an ontology by acquiring an initial software requirement; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts, wherein the ontology concepts at least comprise software and hardware concepts and an environment concept; constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts; integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software. The body is thought to be conceptual clear and definite specification, from this, introduce the demand extraction in-process of unmanned aerial vehicle flight control and management system software with the body concept, because unmanned aerial vehicle flight control and management system software are closely relevant rather than the geographical environment of locating when using, this embodiment is through constructing the geographical body, geographical environmental factor when software demand extraction has fully been considered when software demand extraction, the relevant information of geographical environment has been merged into, the demand extraction of unmanned aerial vehicle flight control and management system software is carried out to software demand extraction body after the integration, unmanned aerial vehicle flight control and management system software's demand extraction accuracy has been promoted, thereby unmanned aerial vehicle flight control and management system software's of final development quality has been promoted.
Drawings
FIG. 1 is a flowchart illustrating a software requirement extraction method according to an embodiment;
FIG. 2 is a flowchart illustrating a software requirement extraction method according to another embodiment;
FIG. 3 is a schematic flow chart of ontology quality evaluation provided by an embodiment;
FIG. 4 is a flowchart illustrating a software requirement extraction method according to another embodiment;
FIG. 5 is a class hierarchy diagram of ontology concepts in the initial software requirement extraction ontology provided by one embodiment;
FIG. 6 is a class hierarchy diagram of geographic concepts in a geographic ontology provided by an embodiment;
FIG. 7 is a flowchart illustrating a software requirement extraction method according to another embodiment;
FIG. 8 is a flow diagram that illustrates a computer device that infers an associative relationship between a target ontology concept and a target geographic concept, according to an embodiment;
FIG. 9 is a block diagram illustrating an integrated software requirement extraction ontology, according to an embodiment;
FIG. 10 is a flowchart illustrating a software requirement extraction method according to another embodiment;
FIG. 11 is a conceptual hierarchy diagram of a generalized ontology provided by one embodiment;
FIG. 12 is a block diagram illustrating an initial software requirement extraction ontology, according to an embodiment;
FIG. 13 is a flowchart illustrating a software requirement extraction method according to another embodiment;
FIG. 14 is a partial structural schematic diagram of a geographic ontology, provided by an embodiment;
FIG. 15 is a block diagram illustrating an exemplary software requirement extraction apparatus;
fig. 16 is an internal structural diagram of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The software requirement extraction method, the software requirement extraction device, the computer equipment and the readable storage medium aim at solving the technical problem that in the traditional technology, in the actual software development process, the quality of the developed unmanned aerial vehicle flight control and management system software is poor due to the fact that the accuracy of unmanned aerial vehicle flight control and management system software requirement extraction is poor. The following describes in detail the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems by embodiments and with reference to the drawings. The following specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
It should be noted that, in the software requirement extraction method provided in the embodiment of the present application, an execution main body may be a software requirement extraction device, and the software requirement extraction device may be implemented as part or all of a computer device by software, hardware, or a combination of software and hardware. In the following method embodiments, the execution subject is a computer device, which may be a flight controller; it can be understood that the software requirement extraction method provided by the following method embodiments may also be applied to a terminal, and may also be applied to a system including the terminal and a flight controller, and is implemented through interaction between the terminal and the flight controller.
Please refer to fig. 1, which illustrates a flowchart of a software requirement extraction method according to an embodiment of the present application. The embodiment relates to a specific implementation process for constructing a software requirement extraction body for requirement extraction of unmanned aerial vehicle flight control and management system software. As shown in fig. 1, the software requirement extraction method of this embodiment may include the following steps:
and step S100, acquiring an initial software requirement extraction body.
The initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and the incidence relation among the ontology concepts.
Wherein, the ontology concept at least comprises a software and hardware concept and an environment concept.
In this embodiment, as an implementation manner, the computer device obtains a generalization layer related concept and an association relationship, that is, obtains a plurality of generalization concepts and an association relationship between the generalization concepts, where the generalization concepts may be top-level concepts such as "system", "environment", and the like when the software requirement is extracted, and the association relationship between the generalization concepts may be a parent-child relationship. Further, the association relationship between the generalized concepts can be enriched, for example, other relationships except parent-child relationships are added to obtain a generalized ontology concept space association table.
The computer equipment acquires the related concepts of software and hardware, the environmental concepts and the association relations among the concepts of the unmanned aerial vehicle flight control and management system, and the related concepts of the historical defect of the software demand and the association relations among the concepts. Wherein, the unmanned aerial vehicle Flight Control and management System software and hardware related concepts such as FCM Computer (Flight Control management Computer), Servo System (Servo System), and other software concepts and hardware concepts; environmental concepts such as longitude, latitude, terrain environment and other concepts related to the software environment of the unmanned aerial vehicle flight control and management system; software requirements historical defect related concepts such as Environmental Error, Safety Error, and so on.
And the computer equipment fuses the generalized ontology concept space association table, the unmanned aerial vehicle flight control and management system software and hardware related concepts, the environmental concepts and the association relations among the concepts and the software demand historical defect related concepts through ontology tool prot g e software to obtain an initial software demand extraction ontology.
In this embodiment, as an implementation manner, the computer device specifically performs specification description on a plurality of ontology concepts, association relations, attributes, and the like of the initial software requirement extraction ontology through an ontology language OWL-DL.
Furthermore, the computer device can store the initial software requirement extraction ontology into an owl format, and perform inference verification on the stored ontology file through an inference engine plug-in pellet included in the prot g é software, so as to verify whether the ontology concept, the association relation and the like in the ontology file conflict or not, thereby improving the reliability of the initial software requirement extraction ontology.
In other embodiments, the initial software requirement extraction ontology may also be created manually and imported into the computer device after being created, and this embodiment is not limited in this embodiment.
And S200, constructing a geographic ontology according to the acquired geographic information basic data.
The geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts.
In this embodiment, the computer device obtains geographic information metadata, that is, geographic information basic data. As an embodiment, the computer device may obtain the portion associated with the demand extraction of the drone flight control and management system software from a website related to the field of digital geographic data. As an implementation manner, according to the role of the geographic information basic data in this embodiment, according to the division of the topic types by the national standard GB/T19710-.
Further, in order to improve the correlation between the acquired geographic information basic data and the requirement extraction of the unmanned aerial vehicle flight control and management system software, the geographic data acquired by the computer equipment can be screened by a domain expert, the part associated with the requirement extraction of the unmanned aerial vehicle flight control and management system software is reserved, and the computer equipment stores the screened geographic data, so that the geographic information basic data is obtained.
In this embodiment, the computer device selects a plurality of geographic concepts and an association relationship between the geographic concepts according to the obtained geographic information basic data, and constructs a concept system having a hierarchical structure, that is, a geographic ontology. As one embodiment, each concept is described by a set of attribute sets that include attribute information for the corresponding concept.
And step S300, integrating the corresponding concepts in the initial software requirement extraction ontology and the geographic ontology according to the association relations to obtain an integrated software requirement extraction ontology.
The integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software. The integrated software requirement extraction ontology is a software requirement extraction ontology considering geographic environment factors. In this embodiment, because the geographic ontology and the initial software requirement extraction ontology relate to different fields, in order to enable the geographic ontology and the initial software requirement extraction ontology to interact, a mapping relationship is established between concepts of the geographic ontology and the initial software requirement extraction ontology, so as to realize ontology integration.
As an implementation manner, the computer device extracts corresponding concepts in the ontology and the geographic ontology from the initial software requirement according to the association relationship between the ontology concepts and the association relationship between the geographic concepts, and integrates the ontology concepts and the geographic concepts that are similar, identical, or semantically related. Specifically, the computer device may calculate the initial software requirement to extract the similarity between each ontology concept in the ontology and each geographic concept in the geographic ontology according to the association between each ontology concept and the association between each geographic concept, and integrate the ontology concept and the geographic concept with the similarity greater than the preset threshold by comparing the similarity with the preset threshold. Furthermore, the computer device can perform semantic analysis on the ontology concept and the geographic concept with the similarity lower than the preset threshold, if semantic relation exists between the ontology concept and the geographic concept, the ontology concept and the geographic concept are integrated, and if semantic relation does not exist between the ontology concept with the similarity lower than the preset threshold and the geographic concept, the ontology concept and the geographic concept are not integrated, namely the integrated software needs to extract the ontology without the ontology concept and the geographic concept, so that omission can be avoided, and the accuracy of ontology integration is improved.
In the embodiment, the geographical ontology is integrated into the initial software requirement extraction ontology to enrich the semantics of the initial software requirement extraction ontology in the aspect of geographical information, so that the knowledge completeness of the integrated software requirement extraction ontology is enhanced.
The embodiment extracts the body by acquiring the initial software requirement; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts; constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts; integrating corresponding concepts in the initial software requirement extraction ontology and the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software. The body is thought to be conceptual clear and definite specification, from this, introduce the demand extraction in-process of unmanned aerial vehicle flight control and management system software with the body concept, because unmanned aerial vehicle flight control and management system software are closely relevant rather than the geographical environment of locating when using, this embodiment is through constructing the geographical body, geographical environmental factor when software demand extraction has fully been considered when software demand extraction, the relevant information of geographical environment has been merged into, the demand extraction of unmanned aerial vehicle flight control and management system software is carried out to software demand extraction body after the integration, unmanned aerial vehicle flight control and management system software's demand extraction accuracy has been promoted, thereby unmanned aerial vehicle flight control and management system software's of final development quality has been promoted.
Fig. 2 is a flowchart illustrating a software requirement extraction method according to another embodiment. On the basis of the embodiment shown in fig. 1, as shown in fig. 2, the software requirement extraction method of this embodiment further includes step S410 and step S420, specifically:
and step S410, adopting a preset evaluation index to perform body quality evaluation on the integrated software demand extraction body to obtain an evaluation result.
Wherein the evaluation index comprises a body cohesion degree measurement index.
In this embodiment, as an implementation manner, the ontology quality evaluation includes ontology verification and ontology verification. The ontology verification comprises ontology content evaluation and capability problem evaluation, and the ontology verification comprises ontology classification evaluation and FOCA evaluation; and the FOCA evaluation comprises ontology type verification, ontology cohesion degree measurement index, problem verification and quality verification.
And the computer equipment calculates and obtains an evaluation result of the total quality evaluation verification of the integrated software requirement extraction ontology, namely an evaluation result, according to the ontology content evaluation result, the capability problem evaluation result, the ontology classification evaluation result, the ontology type verification result, the ontology cohesion degree measurement index result, the problem verification result and the quality verification result.
Referring to fig. 3, fig. 3 is a schematic flow chart of ontology quality evaluation in the present embodiment.
As an embodiment, the computer device may present the evaluation questions related to the ontology content evaluation, the evaluation questions related to the capability question evaluation, and the evaluation questions related to the ontology classification evaluation, and in response to the user input option, the computer device combines the user input and the preset scoring rule to obtain an ontology content evaluation result, a capability question evaluation result, and an ontology classification evaluation result, which may be a score. In other embodiments, the ontology content evaluation, the capability problem evaluation and the ontology classification evaluation may also be manually evaluated, and the computer device obtains the final ontology content evaluation result, capability problem evaluation result and ontology classification evaluation result. The evaluation problem related to the ontology content evaluation can relate to the consistency, completeness, accuracy, expandability and sensitivity of the integrated software requirement extraction ontology, and the like. Evaluation problems associated with ontology classification evaluation may involve, among other things, the software requirements after integration extracting the inconsistencies, incompleteness, and redundancies of the ontology.
As an embodiment, the evaluation problem related to the capability problem evaluation may be specifically determined according to the scope and design target of the integrated software requirement extraction ontology. For example, taking the performance problem evaluation performed manually as an example, see table 1, table 1 is a performance problem and an exemplary confirmation that can be adopted when performing the performance problem evaluation.
Figure BDA0002465600810000071
Figure BDA0002465600810000081
TABLE 1
Therefore, through the evaluation questions related to the ability question evaluation, the ability question evaluation is manually carried out to obtain an ability question evaluation result, and the computer equipment acquires the ability question evaluation result.
In this embodiment, the ontology type verification stage defines two types of ontologies: the domain ontology/task ontology and the application ontology, namely the computer device classifies the integrated software requirement extraction ontology according to the classification.
Further, the computer device acquires the integrated software requirement and extracts the ontology cohesion degree measurement index corresponding to the ontology. The ontology cohesion degree measurement index is used for quantitatively evaluating the closeness degree of the relation between the concepts in the software demand extraction ontology after integration, namely the ontology cohesion degree is higher, the tighter the relation between the concepts is, and conversely, the looser the relation is, so that the ontology cohesion degree can reflect the modularization degree of the software demand extraction ontology after integration to a certain degree. More importantly, the cohesion degree of the related concepts in the integrated software requirement extraction ontology is an important index for reflecting the quality of the integrated software requirement extraction ontology.
In this embodiment, the integrated software needs to extract an ontology cohesion degree measurement index corresponding to the ontology, specifically, the comprehensive cohesion degree TCOO is calculated through the ontology module cohesion degree Coh, the ontology cohesion degree AOC, and the leaf node average inheritance depth ADIT-LN.
1) The cohesion degree Coh (M) of the ontology module represents the tightness degree of each concept connection in the module of the software requirement extraction ontology after integration, and the calculation formula is shown as formula 1:
Figure BDA0002465600810000091
wherein M represents an ontology module; n represents the number of nodes in DAG (Directed Acyclic Graph) of M; n (n-1)/2 represents the number of completely connected graph edges in the DAG graph of M; r (ci, cj) represents the relationship between the concepts ci and cj, and if there is a direct or indirect parent-child relationship between ci and cj, R (ci, cj) is 1, otherwise R (ci, cj) is 0. If there is no concept in the ontology module M, coh (M) is 0. If there is only one concept in the ontology module M, coh (M) is 1, since this concept is independent of any other concept, and is itself the most compact structure. From the above formula 1, the cohesion Coh value of the bulk module is in the range of [0,1 ].
2) AOC represents the degree of cohesion of the body, and the calculation formula is shown as formula 2:
Figure BDA0002465600810000092
wherein n is the number of modules divided by the extracted ontology according to the integrated software requirements, and Coh (Mi) is the cohesion degree of the ontology modules Mi.
3) The average inheritance depth ADIT-LN of the leaf nodes represents the depth of the concept hierarchy structure in the integrated software requirement extraction ontology, and further describes the richness and the refinement degree of the concept, and the calculation formula is shown as formula 3:
Figure BDA0002465600810000093
wherein, DOIi represents the inheritance depth of a path i from a root node to a leaf node in the DAG, and the inheritance depth refers to the total number of a certain path from the root node to the upper edge of the leaf node in the DAG; n-TNOP, i.e., the sum of the different path numbers from the root node to the leaf nodes in the DAG.
4) TCOO represents the comprehensive cohesion metric value of the integrated software demand extraction ontology, and the calculation formula is shown as formula 4:
tcoo (o) ═ aoc (o) + β ADIT-ln (o) formula 4
Where α + β is 1, and O denotes an integrated software requirement extraction ontology. The values of the indexes AOC and ADIT-LN are relatively high, which means that the relation connection between the integrated software requirement extraction ontology concepts is tight, and the concept hierarchy of the integrated software requirement extraction ontology is deep, and the concepts are rich and detailed.
And finally calculating by the computer equipment through the formula 1-formula 4 to obtain a comprehensive cohesion degree measurement value corresponding to the integrated software requirement extraction ontology, namely an ontology cohesion degree measurement index. As an implementation manner, the computer device may specifically perform calculation of the ontology cohesion degree measurement index through matlab software, and finally obtain an integrated comprehensive cohesion degree measurement value of the software demand extraction ontology to obtain an ontology cohesion degree measurement index result.
Referring to fig. 3, in the problem verification stage, the ontology type classified by the ontology is extracted according to the integrated software requirement in the ontology type verification stage, and the computer device obtains and displays a preset verification problem corresponding to the ontology type, where as an implementation manner, the verification problem may include "is another ontology reused? "," whether the measure index of the cohesion degree of the ontology is obtained ", and the like, and a question verification result is obtained according to the option input of the user.
Further, the quality verification can obtain a quality verification result of the integrated software requirement extraction ontology through a quality calculation formula, the quality verification result can be a score between [0 and 1], and the higher the score is, the better the quality of the integrated software requirement extraction ontology is.
The computer device obtains the evaluation result of the integrated software requirement extraction ontology according to the ontology content evaluation result, the capability problem evaluation result, the ontology classification evaluation result, the ontology type verification result, the ontology cohesion degree measurement index result, the problem verification result and the quality verification result, as an implementation manner, the evaluation results may be added to serve as a final evaluation result, or the ontology content evaluation result, the capability problem evaluation result, the ontology classification evaluation result, the ontology type verification result, the ontology cohesion degree measurement index result, the problem verification result and the quality verification result may be directly used as a final evaluation result, and no specific limitation is made herein.
And step S420, optimizing the integrated software requirement extraction body according to the evaluation result.
And the computer equipment acquires the evaluation result, and if the integrated software requirement extraction body needs to be optimized according to the evaluation result, the integrated software requirement extraction body is optimized. In other embodiments, if it is determined that the integrated software requirement extraction ontology needs to be reconstructed according to the evaluation result, the integrated software requirement extraction ontology is reconstructed.
In this embodiment, as an implementation manner, the computer device may perform body quality evaluation on the integrated software demand extraction body according to a set time period, and optimize the integrated software demand extraction body according to the obtained evaluation result. From this, guaranteed the quality and the reliability of software demand extraction body after the integration, and then promoted the accuracy and the reliability of unmanned aerial vehicle flight control and management system software's demand extraction.
Fig. 4 is a flowchart illustrating a software requirement extraction method according to another embodiment. On the basis of the embodiment shown in fig. 1, as shown in fig. 4, in the software requirement extraction method of this embodiment, step S300 includes step S310, step S320, and step S330, specifically:
step S310, calculating the similarity between the plurality of ontology concepts in the initial software requirement extraction ontology and the plurality of geographic concepts in the geographic ontology according to the incidence relation between the ontology concepts and the incidence relation between the geographic concepts.
Specifically, first, the computer device obtains a lexical comparison result of a plurality of ontology concepts in the initial software requirement extraction ontology and a plurality of geographic concepts in the geographic ontology, where the lexical comparison process may be performed manually, and the computer device obtains the lexical comparison result. The lexical comparison result of the ontology concept in the initial software requirement extraction ontology and the corresponding geographic concept in the geographic ontology is '1' or '0', wherein '1' represents complete matching, and '0' represents mismatch. And for the ontology concept and the geographic concept of which the lexical comparison result is 1, the computer equipment further calculates the similarity of the ontology concept and the geographic concept of which the lexical comparison result is 0, and directly filters the ontology concept and the geographic concept of which the lexical comparison result is 0.
In this embodiment, the structural comparison between the two concepts is specifically performed by the following formula 5, that is, similarity calculation:
Figure BDA0002465600810000111
wherein ci represents a concept in ontology O1; cj represents a concept in ontology O2; CiH denotes the super class list of ci in hierarchy H; cjH represents the super class list of cj in level H.
In this embodiment, the initial software requirement extraction ontology and the geographic ontology are both ontologies relating to two hierarchies (generalization layer and domain layer), so for concepts of different hierarchies, the similarity value needs to be calculated by combining paths of corresponding hierarchies, that is, the similarity value should be calculated on the same hierarchy.
For example, referring to FIG. 5, FIG. 5 is a class hierarchy diagram of ontology concepts in an initial software requirement extraction ontology. Referring to FIG. 6, FIG. 6 is a class hierarchy diagram of geographic concepts in a geographic ontology. Fig. 5 includes a plurality of ontology concepts corresponding to the flight control and management system software of the unmanned aerial vehicle and parent-child relationships between the ontology concepts, and fig. 6 includes a plurality of geographic concepts and parent-child relationships between the geographic concepts.
Assuming that the initial software requirement extracts an ontology as an ontology O1, assuming that a geographic ontology is an ontology O2, and taking concept agents as an example, ci represents the agents in the ontology O1; cj represents Agent in the body O2, and since CiH in the body O1 is Thing → EventFlow → StaticEntity → System → Agent, and CjH in the body O2 is Thing → System → Agent, the formula 5 is substituted:
Figure BDA0002465600810000112
therefore, the similarity between the software concept Agent and the geographical concept Agent is 0.75.
Taking geographic concept Extent and software concept SpatEn as examples, ci represents SpatEn in ontology O1, cj represents Extent in ontology O2, since CiH in ontology O1 is Impen → SpatEn, and cjH in ontology O2 is DataTypeInfo → Extent, the substitution is given in formula 5:
Figure BDA0002465600810000121
therefore, the similarity between the geographic concept extend and the software concept SpatEn is 0.50.
Step S320, detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold.
The computer device detects whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold. For example, whether the similarity between the ontology concept Agent and the geographic concept Agent is greater than a preset threshold or not, and whether the similarity between the geographic concept extend and the ontology concept SpatEn is greater than a preset threshold or not are detected.
Step S330, if the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold, merging the target ontology concept and the target geographic concept into an integrated software requirement extraction ontology corresponding concept.
In this embodiment, if the similarity between the target ontology concept and the target geographic concept is greater than the preset threshold, the target ontology concept and the target geographic concept are merged into the integrated software requirement to extract the corresponding concept in the ontology.
As an implementation manner, after the computer device calculates the similarity between the target ontology concept and the target geographic concept, the computer device may also manually perform relationship comparison between the target ontology concept and the target geographic concept, where the relationship comparison is to perform similarity analysis on a non-hierarchical relationship in the concept, and if the two have the similarity of the non-hierarchical relationship, add a weight to the similarity between the target ontology concept and the target geographic concept, compare the similarity after the weight is added with a preset threshold, and if the similarity is greater than the preset threshold, merge the target ontology concept and the target geographic concept into an integrated software requirement extraction ontology corresponding to the concept. From this, through calculating the similarity between a plurality of ontology concepts in the initial software demand extraction body and a plurality of geographic concepts in the geographic ontology, can realize the body integration of initial software demand extraction body and geographic information basic data, through integrating geography ontology and initial software demand extraction body, geographic environment factor when fully having considered the software demand extraction when the software demand extraction, the relevant information of geographic environment has been merged into, unmanned aerial vehicle flight control and management system software's demand extraction accuracy has been promoted, thereby unmanned aerial vehicle flight control and management system software's the quality of final development has been promoted.
Fig. 7 is a flowchart illustrating a software requirement extraction method according to another embodiment. On the basis of the embodiment shown in fig. 5, as shown in fig. 7, in the software requirement extraction method according to this embodiment, step S320 further includes step S340 and step S350:
step S340, if the similarity between the target ontology concept and the target geographic concept is not greater than the preset threshold, detecting whether a preset association relationship exists between the target ontology concept and the target geographic concept by using a preset description logic policy.
Step S350, if the target ontology concept and the target geographic concept have a preset incidence relation, combining the target ontology concept and the target geographic concept into an integrated software requirement extraction ontology corresponding concept.
It is understood that two concepts with similarity not greater than a preset threshold (e.g., 0.8) may have semantic relationship of connotation although the literal similarity is low. Therefore, in this embodiment, in order to avoid ontology integration based on similarity only, and since only the equivalent relationship between the measurement concepts is limited, the problem of low ontology integration accuracy caused by lack of accurate description of semantics due to syntax implementation is emphasized, if the computer device detects that the similarity between the target ontology concept and the target geographic concept is not greater than the preset threshold, the computer device detects whether the target ontology concept and the target geographic concept have the preset association relationship by using a preset description logic policy.
Specifically, the computer device can call a reasoning machine plugin pellet of prot g é software through a Java application program interface, analyze the initial software requirements, extract concepts and association relations in the ontology and the geographic ontology, link the reasoning machine to carry out reasoning according to a description logic strategy, namely a preset description logic strategy, and carry out detection matching on the association relation between the target ontology concept and the target geographic concept so as to detect whether the preset association relation exists between the target ontology concept and the target geographic concept.
Referring to fig. 8, fig. 8 is a schematic flowchart of reasoning on the association relationship between the target ontology concept and the target geographic concept in this embodiment. The computer device calls a reasoning machine plug-in pellet according to the description logic strategy shown in fig. 8, detects whether a preset incidence relation exists between the target ontology concept and the target geographic concept, and if the preset incidence relation exists between the target ontology concept and the target geographic concept, merges the target ontology concept and the target geographic concept into an integrated software requirement to extract a corresponding concept in the ontology.
And for the target ontology concept and the target geographic concept with the similarity not greater than the preset threshold, sequentially carrying out corresponding reasoning, including direct reasoning and transfer reasoning, according to condition judgment by using a description logic strategy until the completion of various conditions to obtain the integrated software requirement extraction ontology.
As an embodiment, it is assumed that a concept list of an initial software requirement extraction ontology is shown as an ontology O1 term list in table 2, a concept list of a geographical ontology is shown as an ontology O2 term list in table 2, and a concept list corresponding to an integrated software requirement extraction ontology ON is shown as an ontology ON term list in table 2. FIG. 9 is a schematic diagram of an integrated software requirement extraction ontology structure including multiple integrated concepts and associations.
Figure BDA0002465600810000131
Figure BDA0002465600810000141
TABLE 2
Therefore, when the similarity between the target ontology concept and the target geographic concept is not larger than the preset threshold value, corresponding reasoning is sequentially carried out according to condition judgment by using the description logic strategy, the reasoning comprises direct reasoning and transmission reasoning, the integrated software requirement extraction ontology is obtained, and the ontology integration accuracy is improved.
Referring to fig. 10, fig. 10 is a flowchart illustrating a software requirement extraction method according to another embodiment. On the basis of the embodiment shown in fig. 1, as shown in fig. 10, in the present embodiment, the step S100 includes a step S110 and a step S120, specifically:
step S110, a generalized ontology concept space association table is obtained, and an unmanned aerial vehicle flight control and management system ontology concept dictionary table and a software requirement historical defect concept dictionary table are obtained.
In this embodiment, first, a Generalized Ontology (GO) needs to be established based on the idea of a Knowledge Aided Design System (KADS), and a concept space association table of the Generalized Ontology is further obtained. As an embodiment, obtaining the generalization ontology concept space association table specifically includes steps a and b:
step a, acquiring a plurality of generalization concepts corresponding to the generalization ontology and the incidence relation among the generalization concepts.
And b, constructing a generalized ontology concept space association table based on the knowledge aided design system KADS, the plurality of generalized concepts and the association relation among the generalized concepts.
The development of the initial software requirement extraction ontology needs to be based on various documents, books, industry standards, and domain knowledge and experience data obtained through communication and consultation with domain experts. The ontology has numerous contents, relates to the field of unmanned aerial vehicle flight control and management systems and the field of software engineering, so that a knowledge system can be modeled by means of KADS, computer equipment extracts ontology concepts, concept attributes, concept hierarchies and the incidence relation among the ontology concepts by extracting domain knowledge, and the definitions are expressed by adopting standardized language to obtain an initial software requirement extraction ontology.
In this embodiment, the computer device uses ontology tool prot g e software to establish GO based on KADS, a plurality of generalization concepts, and an association relationship between the generalization concepts. Referring to fig. 11, fig. 11 is a conceptual hierarchy diagram of generalized ontology GO. Wherein, the concept with the mark is a non-terminal concept class, and the rest is a terminal concept class.
Further, the association relationship in the GO constructed by the computer device may be enriched manually, and the computer device obtains the GO with the enriched association relationship, that is, the generalized ontology concept space association table, as shown in table 3.
Figure BDA0002465600810000151
TABLE 3
Secondly, related concepts and incidence relations of software and hardware of the unmanned aerial vehicle flight control and management system are obtained, and a concept dictionary table of the unmanned aerial vehicle flight control and management system body is established. The unmanned aerial vehicle flight control and management system software is a core part of the unmanned aerial vehicle flight control and management system and is responsible for acquiring state information of unmanned aerial vehicle peripheral equipment and information output by an airborne sensor, and resolving control quantity of the peripheral equipment and an execution mechanism in real time according to designed control logic so as to realize control of the whole flight process from running, takeoff, air flight to approach landing of the unmanned aerial vehicle; in addition, a remote control command sent by ground control personnel needs to be received in real time, command action is executed, and meanwhile, the flight state parameters, the position parameters and the state information of the unmanned aerial vehicle subsystem are sent back to the ground measurement and control center through remote measurement information. As an embodiment, the table of the concept dictionary of the software part of the flight control and management system of the unmanned aerial vehicle may be as shown in table 4, and each item in table 4 is a related concept of software and hardware of the flight control and management system of the unmanned aerial vehicle.
Figure BDA0002465600810000152
Figure BDA0002465600810000161
TABLE 4
And finally, acquiring related concepts and association relations of Software requirement history defects (SREP), and constructing a corresponding concept dictionary table. SREP refers to a specific defect that arises in the development stage of software requirements, repeatedly occurs in a certain scenario during the lifetime of the defect, spreads divergently in subsequent design and implementation, and may cause the system (component) to fail to perform the intended function or affect the maintainability of the system, and the defect has generality and commonality in a specific scenario and can be corrected by some means. The core of the SREP is scene, defect expression and solution, so that the three concepts are selected as components of an initial software requirement extraction ontology concept set to obtain a software requirement historical defect concept dictionary table. The software requirement history defect part conceptual dictionary table may be as shown in table 5.
Figure BDA0002465600810000162
TABLE 5
And step S120, constructing an initial software requirement extraction body according to the generalized body concept space association table, the unmanned aerial vehicle flight control and management system body concept dictionary table and the software requirement historical defect concept dictionary table.
And the computer equipment constructs an initial software requirement extraction body according to the generalized body concept space association table, the unmanned aerial vehicle flight control and management system body concept dictionary table and the software requirement historical defect concept dictionary table. Referring to fig. 12, fig. 12 is a schematic structural diagram of an initial software requirement extraction ontology, wherein,
Figure BDA0002465600810000163
the inheritance relationship is represented and the inheritance relationship is represented,
Figure BDA0002465600810000164
representing relationships other than parent-child relationships.
From this, the initial software demand extraction body is obtained in the construction, carries out standardization accurate expression to a plurality of ontology concepts that unmanned aerial vehicle flight control and management system software correspond and the incidence relation between each ontology concept, is favorable to promoting the accuracy of software demand extraction, and can multiplex.
Referring to fig. 13, fig. 13 is a schematic flowchart of a software requirement extraction method according to another embodiment. On the basis of the embodiment shown in fig. 1, as shown in fig. 13, in the present embodiment, the step S200 includes a step S210, a step S220, and a step S230, specifically:
and step S210, acquiring geographic information basic data.
The geographic information basic data is a plurality of geographic concepts selected from the field of digital geographic data and related to the unmanned aerial vehicle flight control and management system software.
Step S220, obtaining an association relationship between a plurality of geographic concepts.
And step S230, constructing a geographic ontology according to the multiple geographic concepts and the incidence relation among the multiple geographic concepts.
In this embodiment, the computer device obtains the geographic information basic data, and constructs a geographic ontology using an ontology tool prot g.
In this embodiment, the geographic information basic data may be a plurality of geographic concepts selected by a domain expert from the digital geographic data field and related to the flight control and management system software of the unmanned aerial vehicle, and the computer device acquires the plurality of geographic concepts. Each concept is described by a set of attribute sets. As shown in table 6, table 6 shows a part of the geographic information basic data, i.e., the geographic concept.
Figure BDA0002465600810000171
TABLE 6
Further, referring to fig. 14, fig. 14 is a partial structural schematic diagram of the geographic ontology.
From this, through constructing the geographical body, geographical environmental factor when software demand extraction has fully been considered when software demand extraction has merged into the relevant information of geographical environment, carries out the demand extraction of unmanned aerial vehicle flight control and management system software based on the software demand extraction body after the integration, has promoted the demand extraction accuracy of unmanned aerial vehicle flight control and management system software to the quality of the unmanned aerial vehicle flight control and the management system software of final development has been promoted.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 15, there is provided a software requirement extracting apparatus including:
an obtaining module 10, configured to obtain an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts;
the building module 20 is used for building a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
the ontology integration module 30 is configured to integrate the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to each association relationship to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
Optionally, the apparatus further comprises:
the evaluation module is used for adopting a preset evaluation index to perform body quality evaluation on the integrated software demand extraction body to obtain an evaluation result; the evaluation index comprises a body cohesion degree measurement index;
and the optimization module is used for optimizing the integrated software demand extraction body according to the evaluation result.
Optionally, the body-integrated module 30 includes:
the similarity calculation operator module is used for calculating the similarity between the plurality of ontology concepts in the initial software requirement extraction ontology and the plurality of geographic concepts in the geographic ontology according to the incidence relation between the ontology concepts and the incidence relation between the geographic concepts;
the first detection submodule is used for detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold value or not;
and the first integration submodule is used for merging the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology if the similarity between the target ontology concept and the target geographic concept is greater than the preset threshold.
Optionally, the body-integrated module 30 further includes:
the second detection submodule is used for detecting whether a preset incidence relation exists between the target ontology concept and the target geographic concept or not by adopting a preset description logic strategy if the similarity between the target ontology concept and the target geographic concept is not larger than the preset threshold value;
and the second integration submodule is used for merging the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology if the preset incidence relation exists between the target ontology concept and the target geographic concept.
Optionally, the obtaining module 10 includes:
the first obtaining sub-module is used for obtaining a generalized ontology concept space association table and obtaining an unmanned aerial vehicle flight control and management system ontology concept dictionary table and a software requirement historical defect concept dictionary table;
and the first construction submodule is used for constructing the initial software requirement extraction ontology according to the generalized ontology concept space association table, the unmanned aerial vehicle flight control and management system ontology concept dictionary table and the software requirement historical defect concept dictionary table.
Optionally, the obtaining sub-module includes:
the acquiring unit is used for acquiring a plurality of generalization concepts corresponding to the generalization ontology and the incidence relation among the generalization concepts;
and the construction unit is used for constructing the generalized ontology concept space association table based on the knowledge aided design system KADS, the plurality of generalized concepts and the association relation among the generalized concepts.
Optionally, the building module 20 comprises:
the second acquisition submodule is used for acquiring geographic information basic data, wherein the geographic information basic data are a plurality of geographic concepts which are selected from the field of digital geographic data and are related to the unmanned aerial vehicle flight control and management system software;
a third obtaining submodule, configured to obtain an association relationship between the multiple geographic concepts;
and the second construction submodule is used for constructing the geographic ontology according to the plurality of geographic concepts and the incidence relation among the plurality of geographic concepts.
The software requirement extraction device provided in this embodiment may implement the software requirement extraction method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again. For specific limitations of the software requirement extraction device, reference may be made to the above limitations of the software requirement extraction method, which is not described herein again. The modules in the software requirement extraction device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, there is also provided a computer device as shown in fig. 16, which may be a flight controller. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the software requirement extraction data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a software requirement extraction method.
Those skilled in the art will appreciate that the configuration shown in fig. 16 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computing device to which the present application is applied, and in particular that the computing device may include more or less components than those shown, or combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts;
constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Ramb microsecond direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts;
constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A software requirement extraction method is characterized by comprising the following steps:
acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts, wherein the ontology concepts at least comprise software and hardware concepts and an environment concept;
constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the incidence relations to obtain an integrated software requirement extraction ontology; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
2. The method of claim 1, wherein after obtaining the integrated software requirement extraction ontology, further comprising:
adopting a preset evaluation index to extract the body from the integrated software requirement extraction body for body quality evaluation to obtain an evaluation result; the evaluation index comprises a body cohesion degree measurement index;
and according to the evaluation result, optimizing the integrated software requirement extraction body.
3. The method according to claim 1, wherein the integrating the initial software requirement extraction ontology and the corresponding concepts in the geographic ontology according to the respective association relations to obtain an integrated software requirement extraction ontology comprises:
calculating the similarity between a plurality of ontology concepts in the initial software requirement extraction ontology and a plurality of geographic concepts in the geographic ontology according to the incidence relation between the ontology concepts and the incidence relation between the geographic concepts;
detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold value;
and if the similarity between the target ontology concept and the target geographic concept is greater than the preset threshold, merging the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology.
4. The method of claim 3, wherein after detecting whether the similarity between the target ontology concept and the target geographic concept is greater than a preset threshold, the method further comprises:
if the similarity between the target ontology concept and the target geographic concept is not larger than the preset threshold, adopting a preset description logic strategy to detect whether a preset incidence relation exists between the target ontology concept and the target geographic concept;
and if the target ontology concept and the target geographic concept have the preset incidence relation, combining the target ontology concept and the target geographic concept into a corresponding concept in the integrated software requirement extraction ontology.
5. The method of claim 1, wherein the obtaining an initial software requirement extraction ontology comprises:
acquiring a generalized ontology concept space association table, and acquiring an unmanned aerial vehicle flight control and management system ontology concept dictionary table and a software demand historical defect concept dictionary table;
and constructing the initial software requirement extraction body according to the generalized body concept space association table, the unmanned aerial vehicle flight control and management system body concept dictionary table and the software requirement historical defect concept dictionary table.
6. The method of claim 5, wherein obtaining the generalized ontology concept space association table comprises:
acquiring a plurality of generalization concepts corresponding to the generalization ontology and an incidence relation between the generalization concepts;
and constructing the generalized ontology concept space association table based on a knowledge aided design system KADS, the plurality of generalized concepts and the association relationship among the generalized concepts.
7. The method according to claim 1, wherein the constructing a geographic ontology according to the obtained geographic information basic data comprises:
acquiring geographic information basic data, wherein the geographic information basic data are a plurality of geographic concepts selected from the field of digital geographic data and related to unmanned aerial vehicle flight control and management system software;
acquiring an incidence relation among the plurality of geographic concepts;
and constructing the geographic ontology according to the plurality of geographic concepts and the incidence relation among the plurality of geographic concepts.
8. A software requirement extraction apparatus, the apparatus comprising:
the acquisition module is used for acquiring an initial software requirement extraction body; the initial software requirement extraction ontology comprises a plurality of ontology concepts corresponding to the unmanned aerial vehicle flight control and management system software and an incidence relation between the ontology concepts, wherein the ontology concepts at least comprise software and hardware concepts and an environment concept;
the construction module is used for constructing a geographic ontology according to the acquired geographic information basic data; the geographic ontology comprises a plurality of geographic concepts and incidence relations among the geographic concepts;
the body integration module is used for integrating the initial software requirement extraction body and the corresponding concept in the geographic body according to each incidence relation to obtain an integrated software requirement extraction body; the integrated software demand extraction body is used for demand extraction of unmanned aerial vehicle flight control and management system software.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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